scenario_id,failure_type,feature_values,true_label,train_accuracy,val_accuracy,precision,recall,f1_score,error_description,root_cause,fix_strategy,severity,extra_info
OVF_0000,Overfitting,"[-0.0273, 1.4999, -0.1729, -3.6226, -2.6471, 0.654, 1.1966, -0.9333, 2.3218, 0.162, 0.2101, 2.5484, -0.8848, 1.7117, -0.1585, -0.4433, -0.9731, 3.8494, 0.1196, -1.1105]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0001,Overfitting,"[0.1904, -4.3148, -2.4883, -4.3586, -0.6069, -0.3142, -1.8697, 0.4492, 2.6361, -3.6693, -1.6987, -3.1301, 0.0344, -0.6582, -0.3885, -0.5098, 1.1539, 0.8876, -0.4114, -1.3528]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0002,Overfitting,"[-0.1756, 0.5265, -0.7188, 2.3362, 0.7462, 0.4796, 0.6855, 0.6687, 1.0065, -4.9593, 3.8447, 0.675, 1.2959, 0.745, 1.8486, 0.0981, 0.7579, -1.6542, -0.731, -1.1918]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0003,Overfitting,"[-2.5303, 2.0833, -1.2295, -0.0361, -0.4125, 1.0508, 2.0741, -0.286, -0.6633, 2.9332, 0.1418, 2.6876, 1.9583, 0.4967, -0.9194, 1.101, 0.2392, 1.7632, -1.0727, 1.2084]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0004,Overfitting,"[0.6964, -0.3089, -1.1417, 0.9126, -0.6027, 2.3627, -0.1702, 0.9553, -1.175, -1.0743, 1.4653, -0.7337, 1.4775, -0.1937, -0.4532, 0.0884, -0.0034, -0.2212, -0.8182, -0.657]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0005,Overfitting,"[0.6705, 1.2281, 0.3764, 0.1451, -0.0824, 0.9673, 1.0391, -1.0719, -0.8158, 2.3111, -0.0347, 1.6806, 0.8179, -0.9021, -0.0758, -1.5538, -1.1388, 1.0954, 0.6222, 1.1089]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0006,Overfitting,"[-1.0919, -3.0376, -1.1756, -1.0902, 2.6529, -2.1851, -0.6578, 0.8902, 2.4415, -3.4573, -0.7049, -1.0863, 0.5533, -0.8951, 0.8545, 0.1718, 1.0306, -1.3179, 0.5108, 0.911]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0007,Overfitting,"[0.1685, 4.9614, 1.3171, 4.0762, -2.2506, 1.3357, 0.3385, 1.3176, -3.1166, 2.9494, 2.3951, 1.6892, 1.1399, -0.1181, -0.2954, -1.0065, -0.3532, -0.4926, 0.1944, -1.4443]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0008,Overfitting,"[0.7251, 0.7638, 0.8004, -0.7218, 0.7654, -0.6825, 0.9531, 0.5162, 0.009, 3.0172, -1.4399, 1.8419, 0.4319, 0.7543, 0.5131, -0.6415, 0.8624, 1.1398, -0.6267, 1.947]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0009,Overfitting,"[-0.8386, -2.7752, -0.6373, -1.6077, 1.0394, 1.0529, 0.4086, -0.8867, 2.6029, -5.6616, 1.791, -0.3611, 1.229, 0.4584, -0.3078, 0.5348, 0.6814, 0.1727, -1.1023, -0.3277]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0010,Overfitting,"[0.2578, -1.5997, -1.9078, 1.4739, 2.6822, -3.3173, -0.9519, -1.2418, 1.5991, -3.6433, 0.2136, -1.4524, -0.1553, -0.8604, 0.0775, 0.3342, -0.2122, -3.2251, 0.072, 0.0172]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0011,Overfitting,"[-2.4388, -1.0355, 0.9654, -0.3506, -0.6712, 7.1785, 0.2654, -0.1343, -1.4076, -1.8509, 3.4038, 1.2797, 0.9262, 1.2361, -0.5828, 1.4227, -0.1032, 2.4015, 0.3905, 0.7122]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0012,Overfitting,"[-0.0566, 0.5794, -1.818, 0.3051, -2.5466, 1.386, -0.1858, 1.219, -0.2152, -2.2392, 1.8167, -1.2894, 0.1436, 0.7597, 0.31, -1.9511, -1.1172, -0.0265, -1.9671, -3.0161]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0013,Overfitting,"[-1.6077, 1.9442, -0.19, 1.0748, -0.2091, -2.0955, -0.8351, 0.1847, -0.8597, 3.1469, -1.306, 0.4346, 0.0068, -0.3574, 2.0895, 2.0236, 0.1518, -0.5551, -0.3198, 0.1686]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0014,Overfitting,"[0.2942, -0.968, -0.9162, -1.5304, -0.4975, 0.7538, -0.503, -0.2344, 0.9195, -1.3357, 0.1354, -0.2228, -0.6905, -0.8318, 0.2635, -0.7838, 0.6897, 0.9301, -1.2923, -0.4249]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0015,Overfitting,"[-1.1437, -4.2472, -0.1285, -3.5955, 2.08, 5.2802, -0.6124, 0.1086, 0.9636, -1.899, 0.5121, 1.4067, -0.2081, -1.8818, 0.3615, -0.0332, 0.2871, 3.2156, 0.9531, 3.5151]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0016,Overfitting,"[-0.1112, 0.6183, 1.0913, -1.6305, -1.8718, 0.3014, 0.1694, -0.9039, 1.6413, -1.8248, 0.9974, 0.6031, 1.2361, 0.6091, -1.4137, -0.7355, -0.9207, 1.4616, 0.5388, -1.821]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0017,Overfitting,"[0.0143, -1.1213, 0.1059, -2.2685, -0.5914, -0.8204, -1.2112, -0.9539, 1.3725, -0.5357, -1.2498, -0.7338, 0.6863, 0.5844, 0.7308, -0.407, 0.8926, 0.8725, -1.9701, -0.5386]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0018,Overfitting,"[-1.3192, -2.4012, 1.2374, -3.1969, -1.8396, 2.2608, 1.0965, -0.203, 0.3815, -0.9976, -0.8419, -1.924, -0.0635, -0.4573, -0.6257, -0.8, 1.2236, 1.9028, -0.0531, -1.1061]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0019,Overfitting,"[-0.1209, -0.285, 0.7224, -0.72, -2.4198, 3.962, -0.7849, 0.4195, -0.413, -2.3297, 2.3324, -0.5088, -0.4375, -0.3728, 0.6483, -0.8875, 0.0147, 1.4558, -0.134, -1.9965]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0020,Overfitting,"[0.0852, 1.5347, 1.206, 1.0702, -2.7316, 2.2818, 0.3241, -2.9911, -0.381, -2.6703, 3.1795, -0.2461, -1.6305, 0.7774, -1.1477, -0.1869, -0.1905, 0.1666, 0.6405, -3.128]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0021,Overfitting,"[0.6794, -1.9491, -1.7258, -3.276, -1.6582, -1.0826, -1.0872, -1.1488, 1.4292, -0.7106, -2.1805, -2.4212, 0.4626, -0.6776, -1.0858, 0.6663, 0.0342, 0.9281, -0.6757, -1.6791]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0022,Overfitting,"[-0.0336, -2.1396, -0.2059, -1.1836, 1.3212, -0.4492, 0.2532, 0.4547, 2.6104, -4.6492, 1.0845, -0.2281, -0.1965, -0.7466, 0.8203, -0.5164, -0.6316, -0.3325, 0.8954, -0.1598]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0023,Overfitting,"[-1.0917, -2.634, -0.294, -3.8479, -2.0145, 2.5507, -0.0056, -0.3871, 0.2839, -0.2864, -1.3938, -1.922, 0.8491, -0.0716, 0.6687, 0.6955, 1.6745, 2.4616, 0.4578, -0.8332]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0024,Overfitting,"[0.057, -0.2456, 0.5383, 0.9884, -0.4614, 0.1989, -0.6435, 0.2686, -0.2174, -1.9249, 1.0518, -1.3164, 0.5078, 1.0725, 0.9278, 1.5285, 2.165, -1.1474, 0.4559, -1.4049]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0025,Overfitting,"[-0.6118, 4.8819, -1.4063, 3.295, -2.7217, 0.1247, -0.7425, -0.037, -3.1429, 4.4112, 0.7676, 0.9521, -0.6924, -0.0831, -1.32, -0.4293, 0.1946, -0.3707, -0.6226, -1.6341]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0026,Overfitting,"[-0.3425, -1.3013, 0.6102, 0.557, 0.7369, -0.9641, 0.059, 0.1705, 1.2307, -3.8665, 1.0923, -1.3951, -0.2066, 0.157, 0.4469, -0.9627, -0.9823, -1.6973, 0.4535, -1.1095]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0027,Overfitting,"[1.6726, 2.6231, 0.5583, -0.7181, -0.2227, -2.2874, 1.2277, 0.419, 2.7367, -0.6493, 0.979, 3.4403, -0.0558, 0.076, -1.2096, -0.705, 0.3741, 1.2061, -1.9937, -0.2924]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0028,Overfitting,"[-1.5709, 0.8354, 1.7325, 1.1231, 0.6881, 3.8435, -0.1073, -1.1268, -0.7148, -0.7597, 3.0215, 2.8283, 0.1429, 2.2313, 0.4477, -1.1939, 0.3696, 1.1864, 1.1731, 1.4635]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0029,Overfitting,"[-0.6691, 1.4252, 0.6779, -0.3299, -0.6584, 0.2027, 1.4712, 1.0399, 0.6727, 0.0767, 0.8411, 1.7578, 1.826, -0.4879, -0.2093, -0.6056, -1.6136, 1.0951, -1.3719, -0.2119]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0030,Overfitting,"[0.5515, 0.9847, 1.0473, 1.7407, 0.6766, 3.1839, 1.0177, 0.3406, 0.1048, -3.0019, 4.1752, 2.6056, -1.3265, 1.1696, 0.2715, 0.3907, -0.9175, 0.3427, -1.1225, 0.4128]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0031,Overfitting,"[-0.7633, -0.8856, 1.073, -1.956, 0.1546, 1.5439, 0.8692, 0.0379, 0.5696, 0.3379, -0.3253, 1.0493, -0.2093, 2.3639, 0.8873, 0.6833, -1.2377, 1.9094, 0.4822, 1.1801]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0032,Overfitting,"[-1.0537, -1.262, -0.1044, -0.8335, 1.3647, 3.8996, 0.7999, -1.0678, 0.0834, -1.0777, 1.6768, 2.1147, 1.7106, -0.1688, -1.6163, 0.9503, -0.3032, 1.8418, -0.0194, 2.3126]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0033,Overfitting,"[-2.5539, 0.5669, -1.1701, 0.1117, -1.6012, 1.975, 0.9496, 0.9343, 0.6562, -3.2778, 2.827, 0.3169, -0.2248, -1.802, -1.4849, -1.3669, 1.1906, 0.5423, 0.4369, -2.0605]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0034,Overfitting,"[0.4259, -0.1107, 1.8043, -1.1102, -0.9087, -0.2404, 0.1897, 0.0191, 0.1842, 0.6581, -0.8869, -0.4674, 0.4879, -0.1909, -0.662, -0.6415, -1.3632, 0.6626, 2.0243, -0.5199]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0035,Overfitting,"[-0.3752, -2.2735, -1.1115, -4.5282, -3.9997, -0.127, -1.2602, -0.3177, -0.5552, 2.6936, -4.5356, -4.6227, 0.5577, 0.2465, -0.6164, 1.2816, 0.2307, 1.8915, 0.0209, -2.5885]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0036,Overfitting,"[0.5136, 0.168, -0.0275, 1.6022, -0.0138, 0.9937, -0.466, -0.5327, -1.2994, -0.2066, 0.8899, -0.638, -2.8723, 1.7723, -1.5947, -1.1699, -0.1476, -0.9448, 0.3232, -0.2815]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0037,Overfitting,"[0.9758, -0.3642, -0.7096, -2.3104, -0.7646, -3.4428, -0.4174, 0.9185, 2.336, -0.4522, -2.0065, -0.8639, 0.0549, -1.2585, -0.2951, -1.2457, -0.9187, 0.2555, 0.5555, -1.2927]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0038,Overfitting,"[-1.0651, 1.3461, 0.0566, -0.7491, -0.8043, -1.2379, 0.0104, -0.3052, 1.4856, -0.4309, 0.3394, 1.2983, -0.187, 0.5297, -1.3118, -0.6095, 0.5578, 0.7774, 1.3902, -0.8244]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0039,Overfitting,"[0.067, 1.2414, 0.7943, -1.5541, -2.0641, -0.0884, 0.7368, 0.5159, 3.2289, -4.5545, 2.7631, 1.5294, -0.5291, -1.2543, -0.2813, -1.5625, 0.884, 1.4373, -2.4242, -2.851]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0040,Overfitting,"[-0.2286, 1.1889, 2.4126, 0.7032, -0.4119, 3.048, 0.7408, -0.9943, -1.2292, 0.8896, 1.6913, 1.8877, -0.191, 0.7846, -0.5132, -2.5623, -1.0022, 1.2839, 0.0409, 0.7409]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0041,Overfitting,"[2.0562, 2.4554, 0.3074, -0.8846, 0.183, -4.3452, -1.0448, -1.1032, 2.9997, 0.2676, -0.5241, 2.7717, -0.2768, 0.8157, -1.9664, -0.2213, -0.8392, 0.5436, -0.365, -0.1616]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0042,Overfitting,"[1.2172, 0.4687, 0.4037, 0.9013, 0.1366, 2.6466, 1.6154, 1.5213, -1.1523, 0.2545, 1.5167, 1.1826, -0.4316, -0.0242, -0.3223, 0.9983, -0.9224, 0.5995, -0.3918, 0.8065]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0043,Overfitting,"[0.6255, -2.7082, 1.9542, 3.276, 6.7882, -4.803, 0.3676, 0.8852, 2.8784, -6.3261, 1.3742, -0.23, 0.1235, -0.5057, -2.1, -0.5924, -1.3993, -5.3378, 0.4999, 2.4133]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0044,Overfitting,"[-0.2588, 1.704, 0.697, 1.566, -0.8139, 4.1111, -0.0884, 1.5986, -1.8491, 0.4898, 2.7819, 2.0101, -0.2955, -0.3338, -0.37, 0.5609, -0.1096, 1.1463, -0.4364, 0.3212]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0045,Overfitting,"[-1.8362, 0.2192, 0.3886, 2.7499, 1.2572, 0.5478, -0.786, 0.508, -2.189, 1.1482, 0.364, -0.4906, -2.1529, 2.493, -1.3312, -1.1034, 0.9567, -1.7838, 0.7081, 1.0091]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0046,Overfitting,"[-0.3598, -1.6928, -0.9638, -3.6805, -3.7191, -2.4109, 1.1174, -1.326, 1.7686, -1.5343, -2.6075, -4.4284, 0.2603, -0.9572, 1.4485, -0.4135, 0.5018, 0.4058, 0.16, -4.3741]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0047,Overfitting,"[0.8869, -1.588, 0.4367, -2.9735, -0.8369, -0.7786, -1.2524, -0.4208, 1.3842, -0.1646, -1.9015, -1.1688, 0.1989, 0.4043, 0.3636, -2.6042, -0.0121, 1.2057, -1.4512, -0.5511]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0048,Overfitting,"[0.486, 0.5913, -0.0936, 3.3853, 1.3545, -0.6116, 0.5566, -1.5473, 0.4646, -4.3906, 3.3355, 0.0375, -0.4711, 1.3258, -1.3355, 1.0827, 1.8877, -2.8449, -0.4136, -1.0138]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0049,Overfitting,"[-0.7878, 0.7757, -1.9793, 0.0415, -0.7186, 4.3119, -1.8011, -0.6208, -1.2595, 0.6656, 1.9583, 2.0347, -0.4721, 0.7479, -0.5427, -0.1681, 0.5008, 2.0703, 0.6381, 0.84]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0050,Overfitting,"[-0.1315, -0.6749, 1.7496, -1.9264, -2.0307, -0.5598, 0.2253, 0.826, 1.0473, -1.2486, -0.6688, -1.7296, -1.6066, 1.3815, -0.3695, -0.4368, -0.1744, 0.5879, -0.0236, -2.2617]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0051,Overfitting,"[0.6158, 3.8149, 0.0005, 2.5843, -1.698, 1.1771, -0.6177, 1.2039, -2.3881, 3.1, 1.3714, 1.7218, -0.4502, 0.6012, -0.3161, -0.1394, 0.1973, 0.2235, 0.0887, -0.5986]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0052,Overfitting,"[0.9322, -1.1634, -0.8245, 1.2949, 1.8457, -0.4602, -0.9489, -0.2366, 1.5424, -4.8961, 2.318, -0.1867, -1.1063, -0.6086, 1.7474, 1.1356, 1.521, -1.8737, 0.4667, -0.275]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0053,Overfitting,"[1.6777, 0.6627, -0.3791, -0.7642, -1.0416, 2.0428, -0.8037, -0.5536, -0.5143, 1.1663, 0.4013, 1.1404, 1.6284, -0.2036, 1.6391, 0.569, 2.5797, 1.7478, -0.0883, 0.2138]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0054,Overfitting,"[0.5007, -2.1907, 0.6988, -4.9887, -2.9031, -1.2865, 0.8904, 0.0498, 1.7402, 0.2148, -3.439, -2.9898, -0.6603, 0.421, 0.5752, 0.0071, -0.357, 2.0066, -1.5179, -2.3032]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0055,Overfitting,"[1.0818, 0.6977, 0.3868, 3.5625, 1.6343, 0.331, 0.3339, -1.3122, 1.0129, -5.9597, 4.8023, 1.0052, 1.3289, 1.091, 1.4314, 0.6221, -0.1345, -2.565, -0.0393, -0.9367]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0056,Overfitting,"[-0.9551, 2.4746, 0.0242, 3.4036, -0.8396, 3.4737, -0.3348, 0.4236, -2.5433, 0.1251, 3.4636, 1.4132, -1.0675, 1.4122, -0.4036, 2.0625, 1.03, -0.3268, -0.6435, -0.4459]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0057,Overfitting,"[0.4184, -1.0329, 1.0519, -1.7082, -0.2628, 2.0796, -0.8304, 1.4016, 0.2843, -0.2567, 0.1021, 0.4746, -1.5031, -0.9981, 0.5225, 0.6505, -0.8083, 1.6974, 0.0853, 0.5891]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0058,Overfitting,"[1.0325, 3.4616, -1.1057, 1.207, 0.0397, -2.2871, 1.3043, 1.1267, -0.6654, 4.546, -1.0855, 2.589, -0.4108, -0.2149, -1.6625, -1.091, -1.7485, 0.2812, 0.7935, 1.0933]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0059,Overfitting,"[-0.3191, 0.7802, 1.9012, -0.1021, -0.3807, 1.123, 0.2132, -0.796, -1.0028, 2.2718, -0.3395, 0.9486, 0.0213, -0.0607, -0.752, 1.076, 0.6713, 1.0441, -0.1601, 0.801]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0060,Overfitting,"[-0.0265, -1.4565, -0.6752, -2.2091, 1.2319, 2.0187, 0.3514, -0.8819, 0.5528, 0.8063, -0.5815, 1.7265, -0.7449, -0.1445, 1.0702, -0.1631, 0.7083, 2.2491, 1.1889, 2.5942]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0061,Overfitting,"[-0.536, 5.027, -0.2235, 3.6422, -0.4652, -2.5958, -0.9354, 0.8081, -1.7774, 4.4142, 0.1363, 2.2619, 1.8382, -0.3493, 1.086, 0.3673, 0.6154, -1.2114, 0.7388, -0.0541]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0062,Overfitting,"[0.9136, -0.4218, -0.0214, 1.415, -0.835, 1.6029, -0.2265, -0.8032, -0.6456, -2.948, 2.1269, -1.6023, -0.2711, -0.7472, 0.3674, 1.4927, -0.8269, -1.1919, -0.878, -1.8871]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0063,Overfitting,"[1.1895, -0.4147, -0.2976, 1.3217, -1.092, 2.7219, -0.2442, -1.2276, -0.5305, -3.816, 3.0646, -1.1961, 0.7012, 1.3757, 0.9641, 0.5974, 0.4535, -0.7423, 1.6137, -2.0658]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0064,Overfitting,"[-0.5286, 3.6372, 0.3088, -1.7514, 0.2111, -4.2297, 0.3997, 0.5864, 4.3833, 0.0702, 0.3342, 5.3796, 0.0213, 1.7022, -0.6514, 1.2383, -1.2968, 2.0786, 0.181, 0.3933]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0065,Overfitting,"[-0.2309, 0.1049, -0.6059, 0.3748, 1.6188, 2.4291, 0.3038, 1.4533, 1.2757, -3.0559, 3.2748, 3.1533, 0.6929, 1.7194, 0.7216, -1.3386, -1.381, 0.9382, -0.786, 1.4641]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0066,Overfitting,"[0.9191, 1.5035, -0.6681, 1.1311, 0.1762, 1.313, 0.0818, -0.2903, -0.5548, 0.5031, 1.516, 1.9979, 0.3217, 0.992, -0.0989, 0.2674, 0.8385, 0.4889, -0.0061, 0.6836]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0067,Overfitting,"[0.5731, 0.0798, 0.1838, 3.0109, 2.4445, -3.1198, -0.0314, -1.7859, 0.6822, -2.622, 1.0228, -0.5358, 0.3011, 2.693, -0.8484, -0.3596, -0.9267, -3.5204, -0.413, -0.02]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0068,Overfitting,"[-0.9219, -0.1126, -0.7217, -0.6912, 0.1967, 0.9084, -0.0834, -1.004, 0.4281, -0.197, 0.393, 1.0331, 0.0693, 0.1768, -1.4496, 0.2073, -0.65, 0.9608, -1.4965, 0.6282]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0069,Overfitting,"[-0.1943, 0.0239, -0.4836, -1.2573, -3.5521, -2.0482, 0.389, -0.7558, -0.5585, 1.3104, -2.5324, -3.9538, 1.6554, -0.6122, 0.2515, 1.0487, -1.2095, -0.4212, -0.3009, -3.6621]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0070,Overfitting,"[2.9491, -2.55, 0.4819, -4.3658, -0.7372, 1.1945, -1.0852, 1.2447, 1.8219, -0.8209, -1.458, -0.4258, -1.3225, 0.5473, -0.8254, -1.3511, -0.6122, 2.6797, -0.3313, 0.1931]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0071,Overfitting,"[-0.4473, 2.8352, 0.4847, 3.129, 0.5887, -1.4881, 1.8733, 1.281, -1.4245, 2.3517, 0.4581, 1.3487, 0.8528, -0.8464, 1.08, 0.0679, -0.6681, -1.4775, 0.9192, 0.4919]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0072,Overfitting,"[-0.2304, -1.3332, -1.8462, -2.3497, -1.2909, -0.1769, 0.0328, -0.9242, 1.9077, -2.6875, -0.0358, -1.159, 1.0352, -0.9295, -0.7585, 0.8902, -0.0487, 0.8261, 0.3438, -1.7768]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0073,Overfitting,"[1.8846, 0.1551, 0.1409, -1.2797, -2.5921, 1.9824, -0.3722, 1.5432, 1.2337, -3.5255, 2.3065, -0.2172, -1.1196, -1.7684, 1.0887, -0.4888, -0.9594, 1.3176, -0.2265, -2.8411]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0074,Overfitting,"[1.3017, 2.477, 0.46, -0.6744, -0.2341, -5.0175, 0.7421, 1.5615, 4.5357, -3.2983, 1.1513, 2.4435, -0.7534, -0.6777, 0.2993, 0.032, -0.6057, -0.1677, 2.1573, -1.9474]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0075,Overfitting,"[1.4642, -1.1536, 0.3721, -2.7399, -2.7621, -1.398, 2.6208, 0.9969, 0.668, 0.1492, -2.4198, -3.1654, 1.7586, 0.3892, -1.112, 0.2806, -0.0502, 0.5909, -0.2997, -2.7222]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0076,Overfitting,"[1.0417, -2.0404, -0.7069, -4.1811, -2.6278, 2.3839, -0.8639, 0.5835, 1.5366, -2.1421, -0.1817, -1.2152, 0.5799, 0.8556, 1.2795, -0.1295, 0.4196, 2.8271, -0.0945, -1.8653]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0077,Overfitting,"[1.0115, -0.6902, 1.2309, 0.1302, 1.2807, -1.5677, 1.9626, 1.3415, 1.4517, -2.1778, 0.3315, -0.0327, -0.4853, 1.685, 0.0037, -0.7425, 1.4817, -0.9937, -1.0589, 0.0891]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0078,Overfitting,"[0.1764, 0.0166, -1.0721, 2.2188, 0.1653, 2.0487, 0.4288, -0.367, -1.5063, -1.3356, 2.0209, -0.5563, 0.0861, -2.9214, 0.6931, -0.8276, 0.0617, -1.1707, -1.0158, -0.3528]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0079,Overfitting,"[-1.693, 2.1561, 0.1799, -1.1451, -1.1815, -1.6874, -0.754, -0.0983, 2.5315, -1.1603, 0.9722, 2.3512, -1.1036, 1.392, -0.2807, -0.9886, -0.0626, 1.3187, 0.1042, -1.2761]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0080,Overfitting,"[-0.0273, 1.4999, -0.1729, -3.6226, -2.6471, 0.654, 1.1966, -0.9333, 2.3218, 0.162, 0.2101, 2.5484, -0.8848, 1.7117, -0.1585, -0.4433, -0.9731, 3.8494, 0.1196, -1.1105]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0081,Overfitting,"[0.1904, -4.3148, -2.4883, -4.3586, -0.6069, -0.3142, -1.8697, 0.4492, 2.6361, -3.6693, -1.6987, -3.1301, 0.0344, -0.6582, -0.3885, -0.5098, 1.1539, 0.8876, -0.4114, -1.3528]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0082,Overfitting,"[-0.1756, 0.5265, -0.7188, 2.3362, 0.7462, 0.4796, 0.6855, 0.6687, 1.0065, -4.9593, 3.8447, 0.675, 1.2959, 0.745, 1.8486, 0.0981, 0.7579, -1.6542, -0.731, -1.1918]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0083,Overfitting,"[-2.5303, 2.0833, -1.2295, -0.0361, -0.4125, 1.0508, 2.0741, -0.286, -0.6633, 2.9332, 0.1418, 2.6876, 1.9583, 0.4967, -0.9194, 1.101, 0.2392, 1.7632, -1.0727, 1.2084]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0084,Overfitting,"[0.6964, -0.3089, -1.1417, 0.9126, -0.6027, 2.3627, -0.1702, 0.9553, -1.175, -1.0743, 1.4653, -0.7337, 1.4775, -0.1937, -0.4532, 0.0884, -0.0034, -0.2212, -0.8182, -0.657]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0085,Overfitting,"[0.6705, 1.2281, 0.3764, 0.1451, -0.0824, 0.9673, 1.0391, -1.0719, -0.8158, 2.3111, -0.0347, 1.6806, 0.8179, -0.9021, -0.0758, -1.5538, -1.1388, 1.0954, 0.6222, 1.1089]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0086,Overfitting,"[-1.0919, -3.0376, -1.1756, -1.0902, 2.6529, -2.1851, -0.6578, 0.8902, 2.4415, -3.4573, -0.7049, -1.0863, 0.5533, -0.8951, 0.8545, 0.1718, 1.0306, -1.3179, 0.5108, 0.911]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0087,Overfitting,"[0.1685, 4.9614, 1.3171, 4.0762, -2.2506, 1.3357, 0.3385, 1.3176, -3.1166, 2.9494, 2.3951, 1.6892, 1.1399, -0.1181, -0.2954, -1.0065, -0.3532, -0.4926, 0.1944, -1.4443]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0088,Overfitting,"[0.7251, 0.7638, 0.8004, -0.7218, 0.7654, -0.6825, 0.9531, 0.5162, 0.009, 3.0172, -1.4399, 1.8419, 0.4319, 0.7543, 0.5131, -0.6415, 0.8624, 1.1398, -0.6267, 1.947]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0089,Overfitting,"[-0.8386, -2.7752, -0.6373, -1.6077, 1.0394, 1.0529, 0.4086, -0.8867, 2.6029, -5.6616, 1.791, -0.3611, 1.229, 0.4584, -0.3078, 0.5348, 0.6814, 0.1727, -1.1023, -0.3277]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0090,Overfitting,"[0.2578, -1.5997, -1.9078, 1.4739, 2.6822, -3.3173, -0.9519, -1.2418, 1.5991, -3.6433, 0.2136, -1.4524, -0.1553, -0.8604, 0.0775, 0.3342, -0.2122, -3.2251, 0.072, 0.0172]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0091,Overfitting,"[-2.4388, -1.0355, 0.9654, -0.3506, -0.6712, 7.1785, 0.2654, -0.1343, -1.4076, -1.8509, 3.4038, 1.2797, 0.9262, 1.2361, -0.5828, 1.4227, -0.1032, 2.4015, 0.3905, 0.7122]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0092,Overfitting,"[-0.0566, 0.5794, -1.818, 0.3051, -2.5466, 1.386, -0.1858, 1.219, -0.2152, -2.2392, 1.8167, -1.2894, 0.1436, 0.7597, 0.31, -1.9511, -1.1172, -0.0265, -1.9671, -3.0161]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0093,Overfitting,"[-1.6077, 1.9442, -0.19, 1.0748, -0.2091, -2.0955, -0.8351, 0.1847, -0.8597, 3.1469, -1.306, 0.4346, 0.0068, -0.3574, 2.0895, 2.0236, 0.1518, -0.5551, -0.3198, 0.1686]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0094,Overfitting,"[0.2942, -0.968, -0.9162, -1.5304, -0.4975, 0.7538, -0.503, -0.2344, 0.9195, -1.3357, 0.1354, -0.2228, -0.6905, -0.8318, 0.2635, -0.7838, 0.6897, 0.9301, -1.2923, -0.4249]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0095,Overfitting,"[-1.1437, -4.2472, -0.1285, -3.5955, 2.08, 5.2802, -0.6124, 0.1086, 0.9636, -1.899, 0.5121, 1.4067, -0.2081, -1.8818, 0.3615, -0.0332, 0.2871, 3.2156, 0.9531, 3.5151]",1,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0096,Overfitting,"[-0.1112, 0.6183, 1.0913, -1.6305, -1.8718, 0.3014, 0.1694, -0.9039, 1.6413, -1.8248, 0.9974, 0.6031, 1.2361, 0.6091, -1.4137, -0.7355, -0.9207, 1.4616, 0.5388, -1.821]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0097,Overfitting,"[0.0143, -1.1213, 0.1059, -2.2685, -0.5914, -0.8204, -1.2112, -0.9539, 1.3725, -0.5357, -1.2498, -0.7338, 0.6863, 0.5844, 0.7308, -0.407, 0.8926, 0.8725, -1.9701, -0.5386]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0098,Overfitting,"[-1.3192, -2.4012, 1.2374, -3.1969, -1.8396, 2.2608, 1.0965, -0.203, 0.3815, -0.9976, -0.8419, -1.924, -0.0635, -0.4573, -0.6257, -0.8, 1.2236, 1.9028, -0.0531, -1.1061]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
OVF_0099,Overfitting,"[-0.1209, -0.285, 0.7224, -0.72, -2.4198, 3.962, -0.7849, 0.4195, -0.413, -2.3297, 2.3324, -0.5088, -0.4375, -0.3728, 0.6483, -0.8875, 0.0147, 1.4558, -0.134, -1.9965]",0,1.0,0.675,0.68,0.675,0.6758,"Model memorizes training data, fails on unseen data.",Unlimited depth Decision Tree memorizes noise.,"Limit depth, add regularization (L1/L2), use dropout, get more data.",High,{'train_val_gap': 0.325}
UND_0000,Underfitting,"[-1.021, -0.8994, 1.2253, -2.0297, 3.0445, -1.0577, -0.2405, -0.3647, -2.5637, -2.4314]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0001,Underfitting,"[3.4112, 0.8472, 2.911, 0.2375, 3.7983, 0.2884, 1.8857, 0.0231, -0.3992, -1.3718]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0002,Underfitting,"[-0.9291, -3.4862, -0.7787, -2.0124, -3.2334, 0.4001, -1.642, 1.3499, 0.2161, -4.2584]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0003,Underfitting,"[0.2667, 2.1351, 1.9142, -0.999, 3.7915, -3.124, 2.969, 0.4227, -0.1004, 2.5023]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0004,Underfitting,"[2.4821, 1.8539, 3.4501, -3.6755, 7.247, 0.3196, -0.1662, -1.5185, 1.351, 1.9689]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0005,Underfitting,"[0.2295, -5.1142, -0.8269, 0.3654, -6.2195, -0.0134, -1.3567, 1.8653, 1.2831, -5.8188]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0006,Underfitting,"[3.1718, -0.9329, 1.5236, 0.879, 1.2629, 0.6483, 0.0632, -1.5417, 1.535, -1.5999]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0007,Underfitting,"[-3.1779, -3.4147, 2.0496, 0.7826, -1.5269, 2.9027, -1.3401, 0.227, 1.377, -0.7388]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0008,Underfitting,"[1.1211, 3.0445, 2.1427, 0.872, 3.2242, 0.1672, 2.1568, 1.8992, -2.2103, 1.4687]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0009,Underfitting,"[2.3836, 0.23, 1.2821, 0.9073, 1.6033, 1.5052, 0.9299, -1.4965, 1.623, 0.0201]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0010,Underfitting,"[1.7864, 0.4863, 1.0095, -1.0147, 4.7614, 0.1344, -0.7031, -1.7067, -3.6836, -3.1776]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0011,Underfitting,"[-0.7412, -1.0114, 1.3147, 2.2011, -2.5356, 1.9023, -0.1505, 2.4762, 0.2023, -0.2831]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0012,Underfitting,"[-5.6251, -6.5462, 2.6205, 2.1963, -0.9604, 2.3476, 4.3687, -2.2936, 0.2319, -5.4214]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0013,Underfitting,"[-2.7875, -0.7366, -1.1915, -1.5679, -3.1343, -0.5727, -1.5716, 3.7374, -2.1237, -1.1161]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0014,Underfitting,"[-0.901, -3.2353, -1.6077, 3.3769, -4.9829, 1.4679, 3.4699, -0.9679, 1.1266, -3.8998]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0015,Underfitting,"[-1.8675, -3.4144, -0.8409, -1.0292, -4.5038, -4.0471, 0.044, 1.479, 2.3842, -1.1476]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0016,Underfitting,"[-2.739, -4.4227, 2.4656, 1.3522, -1.5935, 0.713, -1.6665, -0.0974, 1.2647, -1.5969]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0017,Underfitting,"[2.1414, 2.2028, -1.3294, 0.367, -1.4936, -0.7722, 0.7199, 1.5408, 0.7077, 1.6181]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0018,Underfitting,"[0.8328, -2.2602, 0.9453, -0.7084, -0.297, -3.249, 1.6641, -0.3677, 1.9191, -1.8529]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0019,Underfitting,"[0.4087, 1.0153, 4.9232, 2.3219, 5.993, 0.5555, 0.3392, -1.1248, -1.4441, 1.7633]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0020,Underfitting,"[1.9294, -3.1245, 1.6845, 1.931, -1.463, 0.0971, -1.9197, 0.2406, 0.2884, -3.6817]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0021,Underfitting,"[-0.756, -0.7462, 0.9189, -4.0806, 2.5997, 2.9639, -4.5053, -1.7184, 2.0644, 1.3977]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0022,Underfitting,"[-2.1073, -2.3352, 0.697, 0.0551, -1.486, -4.9567, -0.9169, 1.274, -0.4558, -0.5263]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0023,Underfitting,"[-1.2693, -4.3055, 1.0358, -0.7219, -2.822, 1.4888, -2.7056, 0.5572, 2.124, -2.5563]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0024,Underfitting,"[0.682, -2.2814, -2.3665, -0.8576, -4.9725, -0.8833, 0.0931, -0.0491, 4.092, -0.9877]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0025,Underfitting,"[1.8032, 0.9077, 0.8686, 0.5467, 0.3309, 3.3557, -2.1718, 1.0378, -0.3205, 0.0787]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0026,Underfitting,"[-0.484, 0.0422, -1.0363, 2.1373, -1.4682, 0.957, -1.0073, -1.4875, 0.9804, 1.7065]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0027,Underfitting,"[0.3532, 0.9822, 2.1479, -0.5219, 3.0268, 1.0578, 1.524, -0.1131, 0.4053, 1.1019]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0028,Underfitting,"[1.6466, 4.5672, -1.0731, -3.0783, 1.7878, -0.3029, -0.3341, 2.4052, -0.7876, 3.3152]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0029,Underfitting,"[0.878, 1.7463, -0.445, 3.4686, 0.8646, -0.3891, 0.3961, -1.6145, -1.5132, 1.4044]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0030,Underfitting,"[2.4985, 1.6508, 0.4382, -0.3203, 1.5153, -0.6753, -1.9164, 0.9274, -1.2961, 0.2878]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0031,Underfitting,"[-0.1415, -1.9337, -1.6572, 1.6439, -4.0346, -0.2095, -0.3136, -0.807, 2.2775, -0.4977]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0032,Underfitting,"[0.3436, 1.0776, 3.0117, 0.4067, 5.4479, -0.6547, 2.1609, -1.647, -1.116, 0.7651]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0033,Underfitting,"[0.602, 1.8863, -0.5312, -1.2933, -0.6971, -0.1844, -0.4011, 2.8829, -0.65, 1.1082]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0034,Underfitting,"[0.2052, 0.9528, -0.0788, -1.3368, 2.1889, 0.2424, 0.9538, -0.5474, -1.3021, -0.6347]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0035,Underfitting,"[1.6867, 1.1002, -0.5679, -1.4706, 3.2744, 0.4686, 4.239, -3.0925, 0.6117, -1.1315]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0036,Underfitting,"[0.4711, -1.6756, 2.191, 1.4899, -0.4839, -1.9466, -0.2348, 0.5251, 1.5571, 0.1563]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0037,Underfitting,"[2.1745, 1.2674, 0.7839, -0.0194, 1.7793, 1.1378, 1.3823, -0.6498, 1.0977, 0.6195]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0038,Underfitting,"[2.9203, 2.2486, -1.8505, -2.8133, 3.0764, 0.6836, 2.4707, -1.8841, -1.0955, -2.0134]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0039,Underfitting,"[-0.7897, -1.8516, -1.1127, 2.3886, -3.2589, 1.4559, 0.4827, -0.0619, -0.6089, -2.619]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0040,Underfitting,"[1.0413, 1.8707, 1.2533, -0.5918, 2.3203, 2.5637, 1.1361, 1.6478, -2.1355, -0.7982]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0041,Underfitting,"[2.1516, 1.1426, 1.1231, -0.2794, 2.7558, -0.1631, 1.496, -1.7559, 1.9504, 1.5031]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0042,Underfitting,"[0.5284, -0.2397, 1.8953, -1.0605, 0.6867, -2.3787, 0.5955, 1.2749, 1.9491, 1.3584]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0043,Underfitting,"[0.0821, -1.2581, -2.2024, 1.6924, -2.2787, -2.2158, 0.1115, -2.6417, 1.412, -0.1497]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0044,Underfitting,"[-0.096, 0.5834, 1.3944, 1.0013, 2.7098, 0.1989, 0.2913, -0.9942, -1.5603, 0.1786]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0045,Underfitting,"[-2.7667, -4.5151, 1.4936, -0.8998, 0.1097, -1.7376, -4.7822, -0.3088, -2.3536, -3.8125]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0046,Underfitting,"[-0.2124, -0.2615, 1.0686, -0.2811, 1.1196, -2.645, 1.2783, 1.1047, -1.7674, -1.3469]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0047,Underfitting,"[-0.2022, -1.499, 1.9383, -1.8673, 4.9256, 0.7986, 1.7111, -2.7257, -1.8482, -3.9334]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0048,Underfitting,"[1.9047, 1.1995, 0.6875, -1.6811, 1.5434, 0.2194, -0.1417, 0.4063, 1.2268, 1.0209]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0049,Underfitting,"[-0.4974, 2.7834, 2.7385, -0.5222, 3.3513, 1.267, -1.6257, 3.2186, -3.0343, 2.3685]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0050,Underfitting,"[-1.0379, 1.0673, 2.0835, -1.1389, 5.5849, 0.8621, -2.5081, -2.2344, -1.6958, 1.9258]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0051,Underfitting,"[2.3457, 4.8285, 0.4676, 0.0431, 2.4288, 2.1032, -0.6847, 2.7192, -2.716, 2.4718]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0052,Underfitting,"[0.4365, 0.8476, 2.4905, -4.9016, 7.9693, 1.7667, -4.3611, -4.456, 1.8219, 3.1666]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0053,Underfitting,"[-2.7574, -4.763, -2.4015, -0.3772, -10.3873, -1.2023, -0.8495, 4.7447, 3.2282, -2.4874]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0054,Underfitting,"[0.4653, 0.1461, 2.204, -0.8924, 2.9834, 1.3107, -0.6869, -0.5034, 0.3131, 0.4958]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0055,Underfitting,"[0.2589, -2.1427, -2.4571, 2.4937, -3.861, -1.5688, -1.1547, -2.7552, 2.3338, -0.2682]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0056,Underfitting,"[3.2621, 0.6578, -0.481, 3.0236, -4.9399, -1.2469, -0.5468, 3.8363, 1.3465, 0.6219]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0057,Underfitting,"[1.5646, 3.3246, -1.7151, 3.6158, 0.6257, -0.771, 1.0836, -1.826, -1.289, 2.788]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0058,Underfitting,"[0.3397, 1.1604, -0.627, -1.7543, 2.0829, 1.0808, -2.9314, -2.7507, 2.0212, 3.3114]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0059,Underfitting,"[5.5488, 4.6744, -1.7, -0.2128, 1.834, 0.7888, -1.8294, 0.5791, -2.0687, 0.7645]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0060,Underfitting,"[1.6389, 3.2501, -1.0398, 2.4041, 1.4721, -1.6397, 1.4741, -1.6514, -0.4899, 3.1863]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0061,Underfitting,"[1.5289, 0.5767, 0.2923, -1.4062, 2.9301, 0.7866, 1.7189, -1.6651, -0.3361, -1.5057]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0062,Underfitting,"[-3.1234, -3.8061, 0.6158, -2.5962, -1.6135, 2.7222, -2.883, -0.1499, 2.0921, -1.4187]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0063,Underfitting,"[-1.6311, -2.0063, -2.9055, 2.8605, -5.1415, -0.4672, 0.5802, -1.5382, 1.6078, -0.3492]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0064,Underfitting,"[-1.1915, -4.3887, -0.6469, -0.8986, -5.6217, -1.7493, -2.4829, 1.7903, 2.8966, -2.0603]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0065,Underfitting,"[-0.8384, -2.7805, 1.5389, -1.329, 0.8022, -1.388, -1.9891, -0.5354, 0.1544, -1.9315]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0066,Underfitting,"[1.5772, 1.9854, 1.2193, 3.6623, 0.1411, -0.2779, 3.2676, 2.1378, -2.1906, -0.0859]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0067,Underfitting,"[2.01, 4.5188, -0.6436, -0.3266, 1.4376, -3.2211, 2.3821, 1.8588, -0.535, 3.7802]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0068,Underfitting,"[-1.8226, -0.1134, 0.5983, -0.1332, 0.4245, 2.6561, 3.2626, 0.5448, -0.7449, -1.1671]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0069,Underfitting,"[0.0851, 0.7458, 0.0915, 1.9062, -0.3676, 1.4553, 0.4328, 0.9234, -1.9182, -0.5991]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0070,Underfitting,"[1.6305, -0.6377, 1.6907, -1.139, 4.1613, 0.613, 0.4304, -2.1028, -1.2727, -2.9797]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0071,Underfitting,"[0.0922, -1.4268, -2.2136, -4.2773, 0.2206, 2.4997, 1.4022, -1.3679, -0.4861, -5.1115]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0072,Underfitting,"[-1.2619, -0.7382, 2.5441, 0.2676, 3.8255, 0.4855, 3.3376, -2.4799, 0.4756, -0.0973]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0073,Underfitting,"[0.9563, 1.537, -0.3063, 0.0839, 0.6572, -0.0306, -0.1992, 1.2223, -2.3513, -0.5354]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0074,Underfitting,"[-1.1049, -0.6466, 0.2147, -0.2491, -0.307, -0.2003, -0.6186, 1.2917, -2.0212, -1.5731]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0075,Underfitting,"[-0.8812, -2.1091, 1.7217, -0.0817, -0.7325, 0.5677, 0.5842, 1.644, -0.2312, -2.4241]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0076,Underfitting,"[1.5895, 0.4141, -1.728, -0.2551, -0.7012, -0.065, 1.9095, -1.9498, 2.4703, 0.3734]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0077,Underfitting,"[-2.2922, -4.7277, 0.8353, 0.2585, -2.8649, -4.7696, -1.3103, -0.5502, 2.7559, -0.7368]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0078,Underfitting,"[1.9766, 2.4396, 0.2247, 0.6751, 1.2131, -1.9149, 0.6664, 0.5176, 0.0259, 2.365]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0079,Underfitting,"[0.9268, 1.395, 1.7176, -1.379, 4.3463, 0.0806, 1.2577, -0.4197, -1.4549, -0.2434]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0080,Underfitting,"[-1.8587, 0.4323, 2.6564, -1.9807, 2.3039, 1.8122, 1.5377, 2.5457, -1.0493, 0.099]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0081,Underfitting,"[-0.1594, -4.4306, 2.9958, 2.746, -3.2889, 1.949, -1.6186, 1.4172, 2.3271, -2.3961]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0082,Underfitting,"[-0.2858, 0.1008, 0.8617, -2.6714, 3.583, 1.4274, -3.6073, -2.4372, 0.8146, 1.6497]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0083,Underfitting,"[-0.892, -1.3196, -0.6489, 0.63, -5.4262, -1.293, -0.6439, 4.1494, 0.8967, -0.2337]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0084,Underfitting,"[-0.0787, 1.0067, 0.0003, -0.4418, 0.2539, 1.2298, -1.0205, 0.858, -0.4744, 0.9467]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0085,Underfitting,"[0.0901, -0.38, 4.5286, -0.901, 8.2533, -2.6952, -0.5769, -3.1011, -2.1107, -0.2254]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0086,Underfitting,"[-0.1005, -3.7669, 1.7538, 2.4984, -2.9215, -1.9543, -2.6244, 1.1927, 0.4222, -2.2379]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0087,Underfitting,"[1.4594, 4.0183, -2.6042, -0.3707, 0.0125, 4.4938, 2.0047, 0.7025, -1.4456, 0.6794]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0088,Underfitting,"[-2.2954, -3.56, 3.1929, 2.3201, 0.3191, -2.8953, -1.4072, -1.1182, 0.9891, 0.2907]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0089,Underfitting,"[0.5552, 1.2301, 2.2046, -4.4994, 7.1742, 1.3571, -3.5776, -3.453, 1.3831, 3.0202]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0090,Underfitting,"[-1.1289, 2.4065, 1.5281, 2.0281, 0.9354, 0.9011, -3.5984, 3.3723, -4.39, 2.3198]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0091,Underfitting,"[-2.9049, -3.749, 1.917, -0.0139, -0.3936, -1.9286, -5.0866, 0.5015, -1.5869, -1.6142]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0092,Underfitting,"[0.4949, -0.1987, 0.5294, 2.1649, -0.8421, 0.8223, 0.6461, 1.6298, -2.5845, -2.4847]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0093,Underfitting,"[2.3033, 0.9291, 2.3645, -0.1981, 2.833, 0.3222, 1.7665, -0.136, 1.3562, 0.6854]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0094,Underfitting,"[-0.037, -4.4261, 2.2638, -0.579, -3.086, -1.0341, -1.3833, 2.1249, 3.0946, -2.4174]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0095,Underfitting,"[2.0159, 1.8646, 1.4836, -3.3221, 5.8244, 0.0797, -2.152, -3.022, 2.0829, 3.3011]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0096,Underfitting,"[0.2205, -0.4194, -3.0137, -1.2982, -4.9817, 1.3938, -2.7361, 1.3402, 2.5366, 0.7034]",0,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0097,Underfitting,"[-0.6829, -3.0259, -0.2769, -0.5504, -2.2197, 0.0363, 3.9584, -0.4119, 1.9888, -3.6436]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0098,Underfitting,"[-1.2089, 2.4094, 2.0723, 0.9359, 2.603, 2.6717, 0.0672, 2.1877, -3.2971, 1.4954]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
UND_0099,Underfitting,"[-0.7341, -4.7228, 1.4004, -0.2985, -1.8953, -0.815, -3.6743, -0.3254, 1.5451, -2.8165]",1,0.7543,0.7467,0.7488,0.7467,0.7465,"Model too simple, fails to capture data patterns.",Linear model applied to non-linear complex data.,"Use a more complex model (Random Forest, Neural Net), add polynomial features.",High,"{'model': 'LogisticRegression', 'complexity': 'too_low'}"
ADV_0000,Adversarial_Attack,"[-2.1595, -0.1538, 1.2488, -1.0119, 0.0618, -0.8754, 0.9704, -1.3149, -1.9248, -0.2198, 0.4643, -0.7891, 0.9843, 0.9587, -0.1983]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0001,Adversarial_Attack,"[0.2975, 0.1661, -0.2945, -0.0204, -2.0494, -2.3287, 0.3811, 0.971, -0.505, -0.9629, -1.2286, 0.1694, -1.9897, -1.1195, 1.5592]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0002,Adversarial_Attack,"[1.8392, -1.3487, 1.2331, 1.7485, -1.0796, -0.5357, -0.8978, -0.2363, 0.3896, 1.1905, -0.3184, 0.5905, -2.7206, -1.4704, 0.0443]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0003,Adversarial_Attack,"[0.4869, -0.8221, -0.062, 0.1512, 1.5727, 0.5864, -0.4668, -1.5933, -0.5744, 0.1465, -0.6418, 1.0677, -0.2945, -0.7943, -0.3033]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0004,Adversarial_Attack,"[0.2345, -0.9533, 0.4324, -0.8372, 1.3122, 0.2328, 1.7059, 0.3311, -0.4654, 0.0444, 0.6151, -0.7711, 1.428, 0.3644, 0.2651]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0005,Adversarial_Attack,"[-2.2054, -0.9492, -1.6115, 1.1056, -0.7839, 0.3522, 0.0199, 1.6721, 0.0917, 0.4152, -0.0182, 0.8016, -1.4198, -0.5222, 0.2075]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0006,Adversarial_Attack,"[-0.5146, 0.4975, -0.6998, -0.0087, 0.8953, 0.2917, 0.3629, -1.0627, -0.9741, 1.218, -0.057, 0.6969, -0.2936, -0.5447, -0.4511]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0007,Adversarial_Attack,"[1.667, 0.0727, 1.9, 2.3643, 1.1705, -0.4474, -0.5924, 0.0381, 0.4267, 0.2177, 1.2516, 1.3995, -0.185, -0.658, 0.4417]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0008,Adversarial_Attack,"[-0.7457, -0.13, 0.0494, 1.2216, -1.6112, -2.3982, -1.1113, 2.7641, 0.4948, 0.5147, -0.5546, 0.1644, -1.5183, -0.7486, -1.383]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0009,Adversarial_Attack,"[0.3407, 2.7079, 0.8406, 0.9884, 0.8088, -0.85, -0.8208, 0.5928, -0.1941, -0.7978, 1.1234, 0.7228, 1.6818, -0.7005, -1.2517]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0010,Adversarial_Attack,"[0.6959, -0.0763, 0.3711, 0.8898, 0.1095, -0.7464, -0.306, -1.3047, -0.4615, 0.3056, -0.7506, 1.0582, -0.7715, -0.4552, -2.0672]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0011,Adversarial_Attack,"[3.5694, -1.5631, 0.0422, 1.9177, -1.8319, -0.1616, 0.045, 1.2208, 1.676, -0.5982, -0.2262, -0.7222, -3.0634, -2.974, 0.5939]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0012,Adversarial_Attack,"[-0.3577, 2.2297, -1.1339, 1.2264, -0.2962, -0.5811, 0.5987, 0.6979, 0.3037, -0.4533, 0.1206, -0.7848, 1.9305, 1.4203, 0.1041]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0013,Adversarial_Attack,"[-0.2254, -2.0838, 0.0674, 0.5432, 1.9191, -0.6792, 1.4585, 0.6104, -0.7335, 0.7625, 0.5518, 0.7684, -0.2905, 0.3176, 0.2932]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0014,Adversarial_Attack,"[-0.7365, 0.3982, -0.0657, -1.5214, -0.5916, -0.8274, -1.4708, 0.0994, -0.2531, 0.0327, -0.3474, -0.2263, -0.5762, -0.0715, 0.083]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0015,Adversarial_Attack,"[-0.2209, -0.9091, 0.2403, -1.0788, -0.9725, -0.7081, 0.3041, 0.1572, 0.3634, 1.228, -0.6696, 1.1174, -1.4092, 1.3675, 2.6812]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0016,Adversarial_Attack,"[-1.4033, -0.0943, -0.8685, -0.2043, -1.9629, 0.4283, 1.0828, 1.1937, 0.0522, 0.0182, -0.99, 0.4065, -1.1862, -0.6253, -0.6045]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0017,Adversarial_Attack,"[-0.4928, 0.3408, 0.6449, -0.8772, -0.5681, -0.8784, -0.3473, -0.4461, -0.8188, 0.2469, 1.1618, 1.0347, 0.2974, -0.0441, 0.7514]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0018,Adversarial_Attack,"[-0.3762, -1.1192, 1.3507, 0.3341, 0.9254, -0.4577, 0.7024, 0.2398, -0.6946, 0.2526, -0.2571, -0.7946, 0.2974, 0.1394, 0.5552]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0019,Adversarial_Attack,"[-1.4571, 0.0967, -0.7634, 1.8213, -0.0496, -0.9763, -1.5656, 2.2608, -0.0726, -0.7381, 0.4413, 1.1289, -0.4371, -0.8206, 0.1979]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0020,Adversarial_Attack,"[-0.0629, -0.3088, -1.4357, -1.2213, -1.968, -0.1578, -2.6061, 0.8927, 2.8019, -1.0765, -0.0636, -0.3076, -0.9226, 2.5514, 0.9096]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0021,Adversarial_Attack,"[0.5977, -0.3951, 1.3211, -0.8028, -0.3946, -0.8446, -0.2734, -0.0992, -0.6181, 0.5162, 0.2962, -0.5232, 0.0762, -0.955, 2.0081]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0022,Adversarial_Attack,"[-0.8077, -0.8356, 0.4274, -1.2326, -0.1194, 1.3293, -0.4505, -0.601, -0.4407, 0.5784, -1.0616, -0.7873, -0.7533, -0.2838, 1.2603]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0023,Adversarial_Attack,"[-1.5691, -0.4103, -0.9534, -1.3169, 2.0921, -1.8545, 0.8761, -1.5444, -3.2337, -1.5504, 0.3706, 1.4142, 0.6292, 0.0272, -1.3928]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0024,Adversarial_Attack,"[0.0184, 1.1639, 0.1646, -0.1026, -0.9194, 1.0449, 0.1138, 1.1875, 0.331, 0.3714, 1.116, 0.9291, 0.404, -0.435, -0.0089]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0025,Adversarial_Attack,"[0.5673, 0.4623, -1.2075, -0.0525, 0.696, 1.0718, 0.6551, 2.0235, 1.1557, 1.157, -1.6637, -1.4992, 0.0079, -0.1848, -0.8932]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0026,Adversarial_Attack,"[-0.7284, -1.7985, -0.8029, 1.5507, 0.5476, 0.9475, 1.2674, 0.6537, -0.0012, -0.3817, -0.4316, -0.8519, -0.1473, -0.6347, -1.4809]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0027,Adversarial_Attack,"[0.36, 2.546, -0.0218, 0.6156, -1.0828, -0.8936, 1.237, -0.3133, -0.2747, 1.1299, 1.3084, -0.043, 2.0273, 1.1892, -1.0261]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0028,Adversarial_Attack,"[0.7478, 0.7444, 1.845, -0.8943, 0.0805, -0.5608, 0.4334, -1.4692, -1.3687, 1.8053, 0.2937, 1.1379, 0.2295, -0.3048, -0.2628]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0029,Adversarial_Attack,"[-0.1866, 1.1066, -0.0714, 0.34, 0.9405, -0.0515, -0.1276, -2.563, -0.891, 0.1576, -2.3181, 0.0365, -0.1784, -0.2023, 0.9554]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0030,Adversarial_Attack,"[1.0845, 0.5218, 0.3873, 0.6312, -0.4086, -1.1939, -1.5866, 0.1861, 1.109, 0.1749, -0.8957, -0.3502, -0.9888, 0.279, 0.6739]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0031,Adversarial_Attack,"[0.1756, 2.159, 1.2451, -0.774, -0.2524, -1.0159, 0.119, -1.5405, -1.4837, 0.1235, -0.3942, 1.2177, 0.0392, -0.586, -0.4697]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0032,Adversarial_Attack,"[-0.6281, 0.7176, -0.2836, -1.4451, 1.6196, -1.1846, -0.9037, -0.3945, -1.8928, 0.4052, 1.3906, 0.4687, 1.2323, -0.611, -0.3677]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0033,Adversarial_Attack,"[0.8898, 0.0674, -0.2046, -0.9322, -1.2226, 1.4334, -1.2757, 0.1464, 1.7124, -0.7331, -0.7618, -1.1095, -0.3229, 0.9987, 0.1126]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0034,Adversarial_Attack,"[0.0151, 1.3701, -0.4965, 0.9677, -1.386, -0.9333, 0.6261, 0.7759, 0.1222, 1.279, 0.8349, -0.5323, 0.4022, -0.3053, 0.1078]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0035,Adversarial_Attack,"[-1.7918, 0.1258, -0.422, 0.2819, 0.3416, 0.8902, -0.2342, -0.3796, -0.1459, -0.2245, 0.1941, -1.9532, 0.6195, -0.482, -1.0792]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0036,Adversarial_Attack,"[0.4245, -1.3514, 0.4475, -0.3287, 0.2749, -0.3813, 0.9368, 0.2964, -0.2292, -1.6015, 1.7537, 0.6778, 0.3468, -0.5968, 1.0701]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0037,Adversarial_Attack,"[1.7142, -0.4869, 0.8004, -1.2892, 0.1737, 0.8337, 0.5238, -1.1688, 0.2693, 1.7953, -1.1671, 0.1545, 0.1259, 1.4797, 0.0178]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0038,Adversarial_Attack,"[0.1641, 0.524, 1.9281, 1.0585, 0.6659, 0.3132, -1.2281, -0.7482, -0.2912, 0.1436, 0.0487, 0.3953, -0.0905, -0.4282, 0.6002]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0039,Adversarial_Attack,"[1.0435, 0.3387, 0.1588, -0.9911, -0.3518, 0.5227, 0.7642, -0.1622, 0.8934, -0.4902, 1.0602, -2.1098, 1.6899, 1.5989, 1.6036]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0040,Adversarial_Attack,"[-0.1598, -0.0337, -0.5142, -0.1686, -0.4318, 1.3068, -0.5667, 1.6566, 1.9191, -0.9976, -1.1128, -0.9826, -0.0301, 0.6297, 0.0322]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0041,Adversarial_Attack,"[-1.5991, -0.3639, 0.1894, -0.5447, -0.0186, 0.2923, 0.4843, -0.7273, -1.2869, -1.5238, 0.2848, 0.4485, 0.6319, 0.5923, 1.9479]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0042,Adversarial_Attack,"[-1.9733, 0.7293, 0.5204, 0.9351, -0.3715, 0.221, -1.3658, 0.1993, -0.4752, 1.0409, -1.5033, 2.0424, -1.0868, -1.2243, 1.039]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0043,Adversarial_Attack,"[0.5353, -0.0214, 0.5001, -0.2197, 2.4783, 0.9718, -1.4064, 0.993, 0.5993, 1.1394, 0.6362, 0.41, 0.3766, -0.5953, -1.2228]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0044,Adversarial_Attack,"[-0.0934, -0.4688, -0.2339, 0.23, -1.4449, 0.4792, 0.9361, 1.0417, 0.5638, -0.2007, 1.2489, -0.8458, 0.3228, 0.3251, -0.8707]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0045,Adversarial_Attack,"[0.6281, -1.0188, 1.0703, 0.1648, 0.1972, -1.1534, -0.1588, -1.1542, -0.142, -1.5417, -1.7343, 0.8891, -1.5923, 0.1833, 0.8988]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0046,Adversarial_Attack,"[-0.1323, 0.7365, 0.1251, 0.4991, 1.3784, 0.6556, -1.5936, 0.6068, 0.474, -0.6221, 0.6095, 0.2002, 0.8407, 0.3556, -0.0395]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0047,Adversarial_Attack,"[-0.7187, -0.4231, 1.5077, -2.5044, 0.2033, -0.294, 0.0803, -0.7833, -1.1244, 0.6314, -0.7576, 0.2028, -0.7881, 0.1733, 1.3536]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0048,Adversarial_Attack,"[1.4606, 2.181, 1.2996, -1.849, -1.4447, 1.8341, 2.113, -1.7332, 0.4835, 0.684, 1.6051, 0.2434, 1.98, 1.0986, -0.1532]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0049,Adversarial_Attack,"[-0.5972, -0.8718, 0.9775, -1.2137, 0.6915, 0.6067, 0.5605, -0.1388, -0.7058, 0.7437, 2.5055, 0.5011, 1.4693, 0.6847, 0.1122]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0050,Adversarial_Attack,"[0.5139, -0.008, 0.7073, -0.3821, 0.8198, 1.2208, 0.7139, 1.223, 0.9763, -0.5754, -1.2205, -0.5812, 0.8366, 0.4094, -1.3024]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0051,Adversarial_Attack,"[-1.5946, -0.9936, -0.3557, -0.6587, -0.0824, 1.3601, -0.3575, -0.7163, 0.1149, -1.2456, -0.7295, -1.9566, -0.1718, -0.5641, 0.7304]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0052,Adversarial_Attack,"[-1.0046, -0.0518, -0.647, -0.203, 0.0312, 0.5304, 0.2823, 0.6099, -0.2783, 0.2188, 1.9742, 0.7423, 0.6906, 1.5318, -2.0985]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0053,Adversarial_Attack,"[-0.6187, 1.0984, 1.5996, -0.069, -2.0096, -0.2959, 1.8741, 0.1997, -0.8204, 0.9105, -0.6133, -0.0179, -1.0077, -1.8177, -0.3112]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0054,Adversarial_Attack,"[0.5548, -0.1751, -0.9876, -0.2854, 0.2024, -0.2136, -0.9382, 0.1614, 0.4058, -1.7264, -0.3792, -0.9477, -0.1751, 0.117, -0.2105]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0055,Adversarial_Attack,"[0.915, 1.6686, 0.4509, -0.299, -0.4508, 0.887, -0.0892, -1.7917, -0.9202, 0.0096, 0.4141, -0.6554, 0.1769, -1.5238, -0.9423]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0056,Adversarial_Attack,"[0.1208, 0.549, -0.7747, 0.6472, -1.1137, -0.1123, 0.8085, -0.4201, -0.0491, -0.1791, 0.2706, -0.0986, -0.0799, 0.0494, -0.5805]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0057,Adversarial_Attack,"[-0.5137, -0.3099, 1.4665, -0.1837, -1.0812, 0.1693, 1.7203, 1.015, 0.4427, 0.9304, 0.1695, -0.9737, 0.3771, 0.1122, 0.4998]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0058,Adversarial_Attack,"[0.4379, 2.2262, 0.4549, 0.1313, -0.825, 0.0393, 0.3926, -0.703, 0.1215, -1.9322, 0.6324, 0.762, 0.9637, -0.1148, 0.7716]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0059,Adversarial_Attack,"[-2.1042, -0.8483, -2.0182, 2.0818, 0.3412, -0.2475, 0.7193, 0.603, -1.1712, 0.0466, -0.3971, -2.1616, -0.341, -1.3784, -0.4891]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0060,Adversarial_Attack,"[-0.4728, -0.9112, 0.0454, 0.3462, 1.3269, 0.8019, -1.5135, -0.704, 0.1466, -1.7693, -0.7603, 0.7353, -0.1823, 0.0468, 1.8433]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0061,Adversarial_Attack,"[-0.976, -1.5788, -1.0162, -0.3617, -0.2, 0.5403, 0.2745, 1.6415, 0.8419, 0.9207, 0.6925, -0.6701, -0.0889, 1.2892, 0.2107]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0062,Adversarial_Attack,"[-0.0015, 0.4084, 2.8575, 0.4848, -0.6698, -1.3281, -0.8339, 1.3945, -0.4899, -0.8801, 0.9951, 0.6853, -0.1793, -1.3849, 0.5853]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0063,Adversarial_Attack,"[-0.3403, 0.3695, 0.9683, 1.5184, -0.0796, 0.7659, 1.5302, -1.331, -1.1968, -0.5264, -0.4115, -0.6268, -0.0823, -1.0426, -0.7714]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0064,Adversarial_Attack,"[-0.6728, 0.1512, -0.0415, 0.3724, -0.9269, 1.1791, 0.2811, -0.4038, 0.5147, -0.4023, -0.2391, -0.6312, 0.1806, 0.0891, 2.1047]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0065,Adversarial_Attack,"[1.2422, -0.4918, -1.0114, 0.9123, -0.799, 0.0947, -0.7885, -0.9047, 0.7845, -0.222, -1.4961, -0.2383, -1.6004, -1.041, -0.3408]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0066,Adversarial_Attack,"[0.2202, -0.5892, -0.0333, -0.0506, 0.421, -0.7187, -1.696, 0.8102, -0.1002, -0.3008, 0.6453, -0.4932, -0.5831, -0.4607, -0.1188]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0067,Adversarial_Attack,"[-0.2449, 1.3148, -1.9772, 0.122, 1.0294, 0.1263, -0.1685, 0.2963, -0.2218, 0.1718, 0.8083, 1.2392, 0.7805, 0.0728, -2.1362]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0068,Adversarial_Attack,"[1.1823, -0.4741, -0.4536, 1.4532, -1.2519, -1.9117, -1.0489, -1.7537, 0.2566, 0.0925, -1.5362, 0.6615, -2.5036, -0.595, 0.1291]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0069,Adversarial_Attack,"[0.4663, -0.058, 0.4219, -1.0561, 1.3903, 1.7256, 2.3637, 1.6712, 0.6258, 0.1726, -1.5537, 0.4369, 0.316, 0.9814, 0.4092]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0070,Adversarial_Attack,"[1.4557, -1.1478, -0.9087, 1.3283, 0.5752, -0.2666, -0.6013, -0.0198, 0.08, -1.6924, -0.1453, -0.5345, -0.8332, -1.6511, 0.5472]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0071,Adversarial_Attack,"[1.2718, 0.3036, 1.6857, -0.7985, 0.366, 0.5762, -0.3634, -0.6868, 0.4707, 0.4908, -0.4235, -0.2671, -0.0752, 0.2266, -1.0589]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0072,Adversarial_Attack,"[-0.5071, 0.3583, 0.2804, 0.0458, -1.306, -1.595, -0.4453, 0.87, 0.4615, 0.6722, 0.264, -0.4735, -0.1117, 0.2057, -0.779]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0073,Adversarial_Attack,"[-1.5679, 0.4081, 1.1677, 2.5054, -1.9319, -1.5049, 0.0453, 2.2895, 0.1795, 0.1867, 0.4013, -0.5608, -0.3467, -0.3587, -0.44]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0074,Adversarial_Attack,"[-0.5469, -1.1345, -0.6106, 0.6841, 1.969, 0.1821, 0.0632, 0.4239, -0.6563, -0.9391, 0.1626, -0.6037, -0.1722, -1.6894, -0.2661]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0075,Adversarial_Attack,"[1.4597, -0.8863, -0.1876, 0.9429, 0.8741, -0.2518, -0.6569, 0.8139, 0.6612, 0.2254, -0.1479, 0.1457, -0.5455, -1.2468, -1.4758]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0076,Adversarial_Attack,"[1.023, 1.1074, -0.4989, -1.0598, -0.9363, 0.5845, 1.7041, -0.3043, 1.1717, -3.7134, 1.1993, -1.0311, 2.2214, 2.2331, -0.2094]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0077,Adversarial_Attack,"[1.0083, 2.0002, -0.8539, -0.808, -0.6219, 0.8633, 2.2147, -0.169, -0.1333, 0.1906, 0.9003, 0.7754, 1.3567, -0.2403, -0.9442]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0078,Adversarial_Attack,"[0.8898, -1.0821, 0.355, -3.5034, -0.4871, 0.8519, 0.3298, -0.5764, 0.0951, -0.6234, 0.2755, 0.0745, -0.7898, 0.2392, 0.8946]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0079,Adversarial_Attack,"[1.5653, 0.9921, 2.5906, 0.14, 1.062, -0.3151, 1.7219, -1.3547, -0.4285, -0.5942, -0.2354, -1.7215, 1.639, 0.1683, 0.5916]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0080,Adversarial_Attack,"[-0.8028, 0.3346, -0.2943, -0.993, -1.0885, 1.0768, -0.7201, -1.6558, 0.1937, -0.1182, -1.0857, -0.9526, -0.3045, -0.4426, 0.0852]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0081,Adversarial_Attack,"[-2.2351, -1.1683, 1.5763, 0.2934, 0.9912, 0.5532, -0.5543, 1.6768, -0.8841, 0.3998, -0.046, -0.35, -0.4407, -1.1196, 0.5168]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0082,Adversarial_Attack,"[-0.0363, -0.227, -0.3655, -0.1822, 1.1189, 0.5011, 1.758, 0.7798, -0.5738, -0.6422, 0.5294, -0.9107, 1.5473, 0.2588, 0.0063]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0083,Adversarial_Attack,"[1.9128, 0.0618, 1.3982, -0.8696, -0.9201, -0.2446, 0.4349, 0.9217, 2.1102, 0.5658, -0.385, -1.5657, 0.7849, 1.9893, 1.0774]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0084,Adversarial_Attack,"[0.5594, 0.2046, 0.9022, 1.0075, -0.5, -0.51, -0.4556, -0.8925, -0.3496, 0.7659, -0.6349, 1.5523, -0.643, 0.1524, 0.4913]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0085,Adversarial_Attack,"[0.0366, 0.2364, -0.3453, -1.3172, -0.275, -0.2738, 0.7358, -0.356, -0.5035, -0.5173, -0.9912, 0.5372, -0.5552, -0.2361, 0.4547]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0086,Adversarial_Attack,"[1.4273, 0.4948, -1.0943, -0.1405, 0.4276, 1.2152, 0.9628, 1.3367, 1.6519, 0.2445, -0.9679, -2.0563, 1.1723, -0.1147, -1.0404]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0087,Adversarial_Attack,"[-1.1883, -0.2208, -0.2428, 0.6643, -0.9285, 1.8943, 0.7511, -0.2227, -0.4044, -0.7816, 0.4493, 0.6715, -0.7214, -1.0768, -0.2164]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0088,Adversarial_Attack,"[0.2175, 1.173, -2.162, -0.744, 0.9998, -0.809, 0.9041, -1.6161, -1.0656, 1.6779, 1.2326, -0.2953, 2.208, 0.5131, 0.5035]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0089,Adversarial_Attack,"[-0.1939, 0.1697, 0.7073, -0.5169, 3.0299, 0.1519, 0.4386, -2.2507, -1.9645, -1.6602, -1.403, 1.3957, 0.6512, 0.5212, 1.1825]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0090,Adversarial_Attack,"[-1.3677, -0.2632, 1.2894, 0.408, 0.0478, 1.4153, 0.1866, 0.8518, -0.4412, 0.1998, 0.3483, 1.6769, -1.3116, -1.6024, -1.0558]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0091,Adversarial_Attack,"[-1.6696, 1.3831, 0.7643, 0.1123, 0.6982, 1.6411, -0.6002, 0.1912, 0.0614, 0.2683, 0.61, -1.7412, 1.952, -0.522, -0.8744]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0092,Adversarial_Attack,"[-0.3026, 0.748, -0.2866, 0.8149, -1.4428, -1.8047, -0.1014, 1.1301, -0.462, 0.8431, 0.3391, -0.4116, -0.5087, -0.7968, 2.1396]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0093,Adversarial_Attack,"[0.2775, 1.1152, -0.6108, -0.0465, -0.131, -0.4678, 1.3477, -0.1697, 0.1307, -0.9476, 0.8274, -0.829, 1.9666, 0.9551, -1.0072]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0094,Adversarial_Attack,"[1.0749, -0.4892, 1.2301, 1.434, -0.0244, -1.0298, 0.0292, -0.6322, -0.1525, -0.0795, -0.5729, -0.6613, -0.3933, -1.2665, -0.0823]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0095,Adversarial_Attack,"[-0.0806, -0.5567, 0.0949, -1.4464, -1.1124, 1.1086, -1.8757, 1.0728, 1.5537, -0.3726, 0.2676, -0.1951, -1.1046, 0.1841, -1.2247]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0096,Adversarial_Attack,"[0.4934, -0.1295, -0.4348, -0.4052, 0.3092, -0.0347, 0.5755, -0.7079, -0.6762, -0.4811, 0.7785, 0.3829, 0.4743, -0.3304, 2.2193]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0097,Adversarial_Attack,"[0.3915, -1.356, 1.7571, -1.4202, -1.1359, 0.0703, -0.7594, 1.1278, 1.0368, -0.0917, -0.7943, 1.323, -1.6473, 0.1189, -1.3321]",1,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0098,Adversarial_Attack,"[-0.5843, 0.7378, 0.9378, -0.3633, -0.5435, 0.5889, -1.9421, 0.0217, 0.6799, -0.0974, -0.1499, -0.6725, -0.0819, 0.2387, 2.5841]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
ADV_0099,Adversarial_Attack,"[-0.197, 1.5189, 1.2478, 0.7976, -0.8843, -0.5534, 0.1652, 0.551, 0.5848, -1.2656, 0.5357, -0.5504, 0.7953, 0.7792, -1.9652]",0,0.9111,0.9,0.9,0.9,0.9,Tiny input perturbation (ε=0.2) caused misclassification.,"Model has sensitive decision boundaries, not robust to small noise.","Adversarial training, input preprocessing, ensemble defenses, certified robustness.",Critical,"{'epsilon': 0.2, 'clean_acc': 0.9111}"
BIA_0000,Bias,"[0.0, 64.8806, 3.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0001,Bias,"[1.0, 59.4622, 14.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0002,Bias,"[0.0, 68.3155, 4.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0003,Bias,"[0.0, 59.9432, 9.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0004,Bias,"[1.0, 29.9896, 11.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0005,Bias,"[1.0, 69.3463, 9.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0006,Bias,"[0.0, 28.6728, 0.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0007,Bias,"[1.0, 62.5774, 2.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0008,Bias,"[0.0, 33.987, 17.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0009,Bias,"[0.0, 59.1184, 4.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0010,Bias,"[1.0, 6.1797, 1.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0011,Bias,"[1.0, 37.5147, 0.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0012,Bias,"[0.0, 50.8552, 15.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0013,Bias,"[0.0, 22.5405, 19.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0014,Bias,"[0.0, 60.6418, 17.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0015,Bias,"[0.0, 43.5257, 2.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0016,Bias,"[1.0, 61.5755, 8.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0017,Bias,"[0.0, 40.8827, 15.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0018,Bias,"[0.0, 76.5838, 0.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0019,Bias,"[0.0, 55.1973, 4.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0020,Bias,"[1.0, 58.9989, 18.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0021,Bias,"[0.0, 48.7677, 18.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0022,Bias,"[0.0, 17.6991, 8.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0023,Bias,"[0.0, 49.8632, 16.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0024,Bias,"[0.0, 53.1257, 13.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0025,Bias,"[1.0, 52.9687, 4.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0026,Bias,"[0.0, 42.037, 15.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0027,Bias,"[0.0, 82.5641, 8.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0028,Bias,"[0.0, 36.4445, 18.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0029,Bias,"[1.0, 62.1209, 8.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0030,Bias,"[0.0, 54.5395, 1.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0031,Bias,"[1.0, 52.9113, 4.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0032,Bias,"[0.0, 58.0498, 7.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0033,Bias,"[1.0, 45.3633, 16.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0034,Bias,"[0.0, 35.1291, 16.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0035,Bias,"[1.0, 26.2415, 8.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0036,Bias,"[0.0, 55.9201, 12.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0037,Bias,"[0.0, 67.9486, 7.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0038,Bias,"[0.0, 49.6371, 15.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0039,Bias,"[0.0, 46.7902, 14.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0040,Bias,"[0.0, 40.6435, 0.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0041,Bias,"[0.0, 42.2358, 19.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0042,Bias,"[0.0, 60.9144, 16.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0043,Bias,"[0.0, 57.668, 2.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0044,Bias,"[0.0, 62.7533, 2.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0045,Bias,"[0.0, 67.5909, 12.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0046,Bias,"[0.0, 56.1473, 18.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0047,Bias,"[0.0, 42.6813, 19.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0048,Bias,"[0.0, 82.1772, 0.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0049,Bias,"[0.0, 38.7231, 16.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0050,Bias,"[0.0, 32.4483, 17.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0051,Bias,"[0.0, 67.0982, 15.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0052,Bias,"[1.0, 43.1167, 5.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0053,Bias,"[0.0, 27.8163, 9.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0054,Bias,"[0.0, 43.0659, 10.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0055,Bias,"[0.0, 47.1969, 18.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0056,Bias,"[0.0, 35.4258, 13.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0057,Bias,"[0.0, 56.4443, 16.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0058,Bias,"[0.0, 63.8695, 1.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0059,Bias,"[1.0, 47.2566, 14.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0060,Bias,"[1.0, 26.6513, 15.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0061,Bias,"[0.0, 49.4797, 6.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0062,Bias,"[0.0, 48.9425, 15.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0063,Bias,"[0.0, 50.4033, 16.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0064,Bias,"[0.0, 61.1314, 10.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0065,Bias,"[0.0, 47.9897, 18.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0066,Bias,"[0.0, 52.7153, 3.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0067,Bias,"[0.0, 47.1928, 13.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0068,Bias,"[1.0, 40.6529, 4.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0069,Bias,"[0.0, 57.2975, 10.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0070,Bias,"[1.0, 44.6106, 2.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0071,Bias,"[0.0, 45.2539, 7.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0072,Bias,"[0.0, 47.4058, 16.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0073,Bias,"[0.0, 58.7315, 13.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0074,Bias,"[1.0, 51.1523, 4.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0075,Bias,"[0.0, 55.1976, 18.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0076,Bias,"[0.0, 62.9071, 16.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0077,Bias,"[0.0, 65.9822, 12.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0078,Bias,"[0.0, 36.728, 0.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0079,Bias,"[0.0, 53.5818, 14.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0080,Bias,"[1.0, 60.0977, 7.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0081,Bias,"[0.0, 54.3375, 3.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0082,Bias,"[0.0, 39.9256, 3.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0083,Bias,"[1.0, 86.8295, 0.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0084,Bias,"[0.0, 63.7449, 3.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0085,Bias,"[0.0, 31.8546, 14.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0086,Bias,"[1.0, 43.5605, 12.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0087,Bias,"[0.0, 73.1316, 12.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0088,Bias,"[0.0, 47.786, 15.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0089,Bias,"[0.0, 34.1988, 8.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0090,Bias,"[0.0, 29.1564, 17.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0091,Bias,"[0.0, 29.453, 9.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0092,Bias,"[1.0, 47.3756, 5.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0093,Bias,"[0.0, 41.4323, 2.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0094,Bias,"[0.0, 57.7391, 3.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0095,Bias,"[0.0, 39.8976, 10.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0096,Bias,"[0.0, 40.9668, 7.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0097,Bias,"[1.0, 59.8782, 12.0]",1,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0098,Bias,"[0.0, 55.7361, 17.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
BIA_0099,Bias,"[0.0, 51.8401, 3.0]",0,1.0,0.53,0.5293,0.53,0.5296,Model discriminates against females in hiring predictions.,Training data contained gender-based hiring bias.,"Balance data, fairness constraints (equalized odds), remove sensitive attributes.",Critical,"{'male_rate': np.float64(0.5204), 'female_rate': np.float64(0.2532)}"
IMB_0000,Class_Imbalance,"[0.1269, -3.3055, -0.0856, -2.0189, 1.278, -4.0383, -0.7391, -2.679, 1.968, -1.268]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0001,Class_Imbalance,"[2.595, -1.7402, 0.9593, -4.5041, -1.4632, -8.604, 3.8454, -4.9001, -0.4804, -2.24]",1,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0002,Class_Imbalance,"[0.1195, -0.3082, 2.3746, -1.7738, 0.744, -2.6138, 1.0083, -0.4392, 1.0236, 0.917]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0003,Class_Imbalance,"[3.1923, -1.2599, -3.363, 2.7641, 0.7348, -1.0253, 2.4153, -3.1973, -1.5362, -2.6118]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0004,Class_Imbalance,"[-0.2524, 0.207, -2.5044, -0.887, 0.7771, 0.6602, 0.8505, 0.1795, 2.388, 0.5568]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0005,Class_Imbalance,"[1.0349, -1.8599, -1.2102, -0.5467, -1.3605, -1.6311, 2.1672, -1.6293, -1.4664, 0.5091]",1,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0006,Class_Imbalance,"[-0.8476, 1.7053, 0.4027, -2.2578, 1.1682, -2.7511, -0.7838, -0.6459, -1.7897, -1.6909]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0007,Class_Imbalance,"[2.2748, -3.8298, 0.8565, -4.5032, 0.6415, -6.0424, -3.4025, -5.3063, 0.2084, -1.231]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0008,Class_Imbalance,"[0.9385, -1.3669, -0.595, 0.0757, -0.025, -1.4653, 0.4451, -1.6011, -0.1163, -0.965]",1,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0009,Class_Imbalance,"[2.9121, 1.3557, -0.0328, -2.4973, 0.4444, -4.9766, 2.6642, -3.3975, -2.1383, -1.5924]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0010,Class_Imbalance,"[-0.7462, 2.9078, 4.1806, -5.8561, 1.2215, -7.1108, 0.1523, -1.3407, -2.7241, -1.1055]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0011,Class_Imbalance,"[3.5089, -0.5148, -0.1716, -0.4799, 2.3903, -6.2435, 1.7818, -5.0867, -3.3822, -3.548]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0012,Class_Imbalance,"[0.0884, -2.433, -1.6531, 2.2689, 0.7947, 0.5662, 1.0668, -0.4468, 1.228, -0.3474]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0013,Class_Imbalance,"[0.2016, -3.2522, 0.5102, -1.3466, 1.0674, -2.0479, -1.5346, -1.7438, 1.6275, 0.5335]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0014,Class_Imbalance,"[-0.4438, -1.1099, -1.6328, 0.9857, -1.6759, 1.717, 0.2291, 0.4958, 0.0855, 0.1925]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0015,Class_Imbalance,"[2.2883, 0.3615, 1.6924, -4.3382, 1.517, -8.4431, 1.998, -4.5801, -3.1249, -2.1702]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0016,Class_Imbalance,"[-0.1596, 4.727, 1.9012, -2.1625, 2.1625, -4.1291, 1.2225, -0.4571, -2.9254, -2.6815]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0017,Class_Imbalance,"[1.8394, 3.1316, 2.3586, -4.4966, 0.5017, -7.1561, -0.3176, -3.6451, -5.7069, -3.2574]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0018,Class_Imbalance,"[-0.9985, -1.35, -2.1631, -1.5851, 1.1433, -2.4141, 1.2107, -0.9949, 3.0473, -1.5435]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0019,Class_Imbalance,"[4.614, -0.0011, -3.4211, 1.9725, 2.7665, 3.0363, 2.5133, -1.5599, -0.3643, 4.3678]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0020,Class_Imbalance,"[2.9963, 0.1759, -0.6555, -0.7475, 1.6971, -5.6586, 2.7333, -4.2746, -3.0495, -3.1451]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0021,Class_Imbalance,"[-0.443, -0.5976, 0.964, -4.9482, 0.4636, -2.6442, -3.9131, -1.4598, 1.3282, 0.3495]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0022,Class_Imbalance,"[-1.1188, 0.6078, -3.5135, 0.3448, 1.4693, 0.0758, 2.5928, 0.5004, 2.937, -1.5023]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0023,Class_Imbalance,"[5.1458, 0.682, -1.6485, -0.8294, 1.7409, -6.8888, 2.5217, -6.4764, -5.122, -4.7226]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0024,Class_Imbalance,"[1.1086, 1.5601, -0.3346, -2.7601, 1.1417, 0.0409, -2.9107, -0.8986, 0.7752, 0.6978]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0025,Class_Imbalance,"[-0.8106, 0.7691, 2.0222, -1.9858, 0.7211, -2.3827, -0.6926, -0.1395, -0.7261, -0.3496]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0026,Class_Imbalance,"[1.6, -2.6887, -1.1871, -2.9044, -0.0021, -2.6599, -1.7033, -3.2666, -0.0255, 0.3003]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0027,Class_Imbalance,"[1.3668, -1.3719, -1.5153, -0.5009, 1.5253, -1.0396, -0.1878, -1.9396, 0.5111, 0.1304]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0028,Class_Imbalance,"[2.3204, 2.972, -1.741, 0.0391, 3.3581, 1.7738, -0.5344, -0.5058, -0.1493, 1.2954]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0029,Class_Imbalance,"[1.4373, 0.9276, -2.5229, -0.2224, 2.1294, -0.3025, 1.177, -1.1568, 2.1044, -0.737]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0030,Class_Imbalance,"[1.9734, -1.1761, 2.1342, -2.8857, 2.0258, -7.4107, 1.2332, -4.1998, -2.1068, -1.7204]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0031,Class_Imbalance,"[0.9757, 3.2016, 1.1176, -1.8857, 2.5036, -4.6921, 2.045, -1.6629, -2.0425, -2.4122]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0032,Class_Imbalance,"[1.5706, 0.6538, -4.2761, -2.0895, -0.8159, 0.1006, 0.5659, -1.6275, 1.6314, -0.5786]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0033,Class_Imbalance,"[1.9375, -0.3322, -1.3528, 0.3962, 0.3482, 3.1535, -1.8425, -0.3515, -0.5791, 3.0306]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0034,Class_Imbalance,"[2.4111, -0.6533, -2.0608, 1.6956, -0.4676, 1.427, 0.091, -1.4088, 0.3414, -0.5547]",1,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0035,Class_Imbalance,"[0.9178, -0.5021, 1.4464, -1.0216, 0.6973, -2.821, 1.9705, -1.1497, 0.5023, 0.2819]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0036,Class_Imbalance,"[0.9764, 2.441, 1.7614, -2.4567, 1.2867, -4.1406, 1.0499, -1.513, -1.822, -1.3992]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0037,Class_Imbalance,"[1.8429, 1.489, -0.8157, -3.5384, 1.1753, 1.0371, -2.7246, -0.9029, -0.7019, 3.6809]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0038,Class_Imbalance,"[3.8676, 3.2351, 2.946, -3.865, 3.0498, -9.5413, 1.522, -5.4675, -5.3352, -4.131]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0039,Class_Imbalance,"[1.2402, 1.2077, 2.176, -2.7155, 0.6022, -4.027, 2.0521, -1.3976, -0.2745, 0.1306]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0040,Class_Imbalance,"[2.0711, 0.2484, 0.4468, 1.6905, 2.4026, 4.5403, -2.3717, 0.694, -1.1748, 5.0748]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0041,Class_Imbalance,"[1.4388, -0.0666, -3.4882, 0.648, 1.3061, 0.8942, 1.6247, -0.9605, 1.0063, 0.2997]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0042,Class_Imbalance,"[1.2298, 3.0042, 2.6463, -2.4655, 1.2411, -3.6796, 0.0254, -1.3233, -2.0158, -1.2413]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0043,Class_Imbalance,"[0.6941, 1.0375, -0.1121, 1.5555, 2.1132, 4.5577, -1.8605, 1.6274, -0.1162, 3.85]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0044,Class_Imbalance,"[-2.2834, 1.7925, 1.6991, -1.4027, 2.4112, -2.3956, 0.4863, 1.0606, 0.0711, -0.8988]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0045,Class_Imbalance,"[-1.0298, -1.7249, -2.7649, 1.0496, 0.0788, 1.1962, 1.711, 0.6126, 2.9893, 0.0619]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0046,Class_Imbalance,"[1.4505, -3.7441, 0.4235, -3.3013, -0.7664, -2.6364, -3.9249, -3.3861, -0.3447, 0.6857]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0047,Class_Imbalance,"[1.8755, -2.1159, -1.4009, 2.704, 0.0039, 0.9031, 2.1583, -0.9765, 1.3613, 0.1955]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0048,Class_Imbalance,"[-1.3165, 1.1359, 3.1366, -1.8602, -0.5495, 0.2551, -1.7111, 1.7003, 0.8224, 1.6114]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0049,Class_Imbalance,"[1.5925, -1.2072, -0.4582, -3.3299, 1.7726, -2.8143, -1.1678, -2.6979, 0.9531, 0.9604]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0050,Class_Imbalance,"[3.5513, 0.7938, 0.7681, -1.0487, -0.9512, -2.4875, 2.2179, -2.5147, -1.2259, -0.1611]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0051,Class_Imbalance,"[0.854, 0.3064, 0.8389, 0.9555, 2.3358, -3.4873, 1.0705, -1.9153, -2.4109, -2.8564]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0052,Class_Imbalance,"[0.4019, 1.9455, -2.6226, 1.3286, 1.2421, 3.6043, 0.7522, 1.4278, 2.5149, 0.9444]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0053,Class_Imbalance,"[-0.6913, 2.3223, -1.8858, -2.5229, 1.0199, 0.2486, 0.085, 0.7191, 4.076, -0.3109]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0054,Class_Imbalance,"[1.1489, -3.4501, -1.0075, -3.5012, 0.3196, -4.687, -0.7831, -3.8353, 1.4257, -0.9539]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0055,Class_Imbalance,"[0.3095, 3.2335, 3.8027, -7.6711, 1.2215, -10.3, 0.4001, -3.5898, -5.1718, -2.6681]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0056,Class_Imbalance,"[4.8785, 0.7561, -0.209, -0.2143, 1.9189, -6.1969, 2.1783, -5.6424, -4.5097, -4.0911]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0057,Class_Imbalance,"[1.5818, -0.855, 3.5328, -2.4077, 0.8023, -4.8854, 1.9538, -2.0119, 0.3404, 0.8157]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0058,Class_Imbalance,"[2.1032, -0.4128, -1.4623, 2.5669, 0.9268, -0.0327, -0.0507, -1.9316, -1.6603, -2.2709]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0059,Class_Imbalance,"[-0.5705, -2.8514, -0.3104, -2.0696, 0.4352, -3.2781, -1.0778, -1.9306, 2.6107, -1.6826]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0060,Class_Imbalance,"[0.5251, 1.5486, 1.2811, -0.8958, 1.4564, -3.001, 1.0834, -1.0035, -1.3676, -1.449]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0061,Class_Imbalance,"[3.5872, -2.8005, -2.0537, 1.1131, 1.3892, 1.0179, 0.4339, -2.5351, -0.8251, 3.1715]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0062,Class_Imbalance,"[1.0463, 0.6028, -0.3663, -0.8435, -0.342, -1.4136, 0.0631, -1.3454, -1.5594, -1.0441]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0063,Class_Imbalance,"[1.3023, -1.7077, -1.8442, 0.095, 0.4767, -4.2708, 5.0007, -2.5963, 1.3534, -2.4524]",1,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0064,Class_Imbalance,"[-0.1012, -1.048, -2.0549, -2.1756, -1.3703, 1.112, -1.1564, -0.1612, 1.9239, 1.6105]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0065,Class_Imbalance,"[0.6432, 3.3169, 1.3209, -2.6881, 2.3792, -4.4918, 1.4902, -1.3445, -1.4692, -1.8213]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0066,Class_Imbalance,"[0.7362, 0.2877, 1.7222, -1.4887, 1.4116, -3.9293, -1.3438, -2.2449, -2.6883, -2.0542]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0067,Class_Imbalance,"[1.0826, -1.5996, 1.7131, -5.0948, -0.0079, -1.4167, -5.0618, -1.9489, 0.1223, 3.2006]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0068,Class_Imbalance,"[2.4227, -0.2699, -1.0107, 1.335, 1.7842, -2.3615, 3.1925, -2.3137, -0.2341, -1.5771]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0069,Class_Imbalance,"[-1.0024, 1.2823, -2.5659, 0.3516, 0.3537, 2.7508, 0.2822, 1.6568, 2.7018, 0.2526]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0070,Class_Imbalance,"[0.9482, -0.7834, -0.8626, -1.0875, 1.6261, 0.205, -0.6955, -0.839, 0.6777, 2.1425]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0071,Class_Imbalance,"[0.1525, 0.7035, -0.4948, 0.4346, 2.0137, 2.8855, -1.1122, 1.1781, 0.7641, 2.9083]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0072,Class_Imbalance,"[2.9363, -2.407, 1.1741, -1.8975, 2.7273, -8.5476, 1.8121, -5.7662, -2.9309, -2.9235]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0073,Class_Imbalance,"[1.0103, 2.4758, 1.2896, 1.944, 1.6808, 0.9222, 1.3117, 0.8642, 0.7761, -0.3612]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0074,Class_Imbalance,"[2.4432, 0.7675, -1.1407, -1.5262, 3.2061, -0.3182, -0.485, -1.7665, -0.8557, 2.4875]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0075,Class_Imbalance,"[1.7574, -3.9626, 0.7046, -1.5241, 0.2917, -3.4317, 1.0601, -2.9353, -0.9869, 2.0464]",1,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0076,Class_Imbalance,"[0.5197, 1.1676, 3.8914, -1.2899, 2.8582, -3.2257, 0.1367, -0.5493, 0.6845, 0.0106]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0077,Class_Imbalance,"[2.5227, 4.344, -2.9858, -2.0846, 2.7375, -1.1328, 4.1121, -1.0181, 2.1033, 0.1559]",1,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0078,Class_Imbalance,"[-0.2693, 2.5663, -1.4218, 1.2009, -0.2042, 2.9402, 0.181, 1.6827, 0.471, -0.2822]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0079,Class_Imbalance,"[0.5002, 0.4849, 0.6556, -1.5506, 2.3279, -5.3027, 1.057, -2.5233, -2.432, -2.6871]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0080,Class_Imbalance,"[1.2215, -1.915, -1.6171, 1.121, 1.5753, 0.8968, 0.7177, -0.8377, 0.9878, 1.7204]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0081,Class_Imbalance,"[0.8242, -3.7729, -0.0809, -1.6946, 1.2502, -4.6147, -1.2274, -3.6121, 1.3423, -2.1271]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0082,Class_Imbalance,"[1.6618, 1.3768, -0.2488, -2.2697, 2.4177, -3.4317, 0.2287, -2.3071, 2.0127, -2.0863]",1,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0083,Class_Imbalance,"[1.0001, -2.3482, 1.5186, -3.7351, 0.8525, -7.0244, 1.2796, -3.7108, -1.3249, -0.7039]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0084,Class_Imbalance,"[2.0145, -3.4823, 0.4958, -3.7861, -0.2253, -3.2973, -3.3965, -3.8085, 0.5201, 0.6957]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0085,Class_Imbalance,"[1.554, -2.7074, 0.7327, -2.112, 1.7564, -2.4307, -1.896, -2.6311, 0.7905, 1.3833]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0086,Class_Imbalance,"[0.8154, 1.8735, 2.5675, -1.7673, 1.08, -3.3219, 0.5385, -1.0041, -1.3604, -0.8292]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0087,Class_Imbalance,"[0.8535, -0.5406, -2.1699, -1.0971, -0.4254, 1.839, -1.3411, -0.4546, -0.0945, 2.1646]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0088,Class_Imbalance,"[1.509, -2.1099, -0.2182, -0.0308, 1.3569, -0.4775, -1.6476, -1.8622, 0.092, 0.753]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0089,Class_Imbalance,"[2.0391, -0.1329, -1.7042, -1.2859, 1.1216, 0.2752, -0.9383, -1.6343, 0.1318, 1.471]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0090,Class_Imbalance,"[-0.4987, -0.4276, -1.0596, -2.499, 0.4923, -1.3694, -1.4116, -0.8795, 1.5874, -0.5393]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0091,Class_Imbalance,"[1.8916, -2.6127, -0.1745, -0.9019, 4.3336, -5.1427, 1.1271, -3.8351, 1.0351, -1.0747]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0092,Class_Imbalance,"[1.4327, 3.27, -0.3503, 1.5362, 1.1458, 1.1109, -0.3853, -0.05, -1.3347, -1.8552]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0093,Class_Imbalance,"[2.9691, 0.906, 0.9066, -1.3972, 2.1335, -5.5164, 3.6924, -3.2984, -0.9928, -1.5305]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0094,Class_Imbalance,"[0.7931, 0.3366, 0.2014, 0.012, 2.2354, -3.3602, 1.9361, -1.6756, -0.9124, -1.8766]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0095,Class_Imbalance,"[0.7449, -1.0577, 0.0196, -0.7651, 1.8233, -4.563, 2.123, -2.4663, -1.0809, -1.7439]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0096,Class_Imbalance,"[0.2663, 0.6429, -2.4838, -0.7736, -0.8919, 2.3245, -0.7763, 0.4352, 2.6771, 0.4331]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0097,Class_Imbalance,"[0.8752, -4.7525, 0.1637, -3.2325, -0.5795, -3.2111, -2.5771, -3.2551, 0.4008, 1.0503]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0098,Class_Imbalance,"[0.0861, 1.3365, -1.0112, -2.1341, 3.3377, -1.6098, 0.8844, -0.4705, 2.8734, 0.2603]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
IMB_0099,Class_Imbalance,"[3.1827, 1.4864, -0.7256, -1.4563, 0.5862, -4.2567, 0.7345, -3.9097, -3.8149, -3.0124]",0,0.9971,0.94,0.9164,0.94,0.9168,High accuracy hides total failure on minority class (5% of data).,95:5 class imbalance causes model to ignore minority class.,"SMOTE oversampling, class_weight=""balanced"", focal loss, cost-sensitive learning.",High,"{'minority_recall_before': 0.0556, 'after_smote': 0.2778}"
GEX_0000,Gradient_Exploding,"[-0.0061, 0.0818, -0.0234, -1.6573, -0.0989, 0.9191, 0.8385, 1.3037, -1.1769, -0.2903]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0001,Gradient_Exploding,"[-0.3191, 1.076, -2.0025, 0.4319, 0.0213, 1.9012, -0.796, -0.9427, -2.2952, -0.0607]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0002,Gradient_Exploding,"[-1.2097, 1.5308, 0.9901, -1.7422, 1.2188, -0.2134, 0.5999, 1.6751, 0.0773, 1.4907]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0003,Gradient_Exploding,"[0.0887, -0.6177, 0.6994, -0.4948, -0.3161, 0.6158, 0.1973, 0.6013, 0.5636, 1.2039]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0004,Gradient_Exploding,"[0.3525, -1.8206, 0.1471, 1.1043, 0.2701, -1.9123, -0.5704, -0.8292, 0.9545, -0.0686]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0005,Gradient_Exploding,"[-1.1618, -0.3009, 0.2424, 1.4912, -1.2095, 0.389, -0.2831, -1.1066, 1.3457, 0.2515]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0006,Gradient_Exploding,"[-0.3216, 0.3819, -0.3235, -1.2497, 0.43, 1.0303, 2.0767, 0.8912, -1.2836, 0.2388]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0007,Gradient_Exploding,"[0.2956, 1.4769, 0.9114, -0.0142, -1.1678, 0.2168, -1.5166, 0.2848, 1.1708, -1.0973]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0008,Gradient_Exploding,"[-0.8096, -0.4738, -1.0568, 0.9007, -0.0145, 0.5463, 0.4241, -1.0296, -0.7458, 0.0064]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0009,Gradient_Exploding,"[0.1896, -2.7032, -2.2516, 1.5965, 0.6779, -0.6541, 1.001, -1.9385, -1.8121, -1.8306]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0010,Gradient_Exploding,"[0.492, -2.1533, 1.6761, -0.6712, 1.0972, -0.4788, -0.526, 1.034, 1.7068, -0.8628]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0011,Gradient_Exploding,"[0.0727, -0.0892, 0.2943, 1.2699, -0.0376, -1.7312, -0.4115, -0.916, 1.2598, 1.4949]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0012,Gradient_Exploding,"[0.6726, -0.1326, -1.5816, 0.438, -0.9745, 1.1071, 1.8999, -0.8211, -1.7458, -0.1204]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0013,Gradient_Exploding,"[-0.6752, -0.7924, -0.9148, -0.0792, -0.308, -1.8936, -0.1445, -0.212, -1.2399, 0.2133]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0014,Gradient_Exploding,"[-0.4931, -1.2565, 1.3133, -0.6672, 0.8132, -0.279, 1.4436, 0.9219, 1.2396, -0.2798]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0015,Gradient_Exploding,"[-1.5566, 1.5008, -1.084, -0.3345, 0.8502, -0.3487, -0.4281, -0.0608, -1.6356, -0.3493]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0016,Gradient_Exploding,"[1.0473, -0.2294, 1.6012, -1.3105, -0.0435, -1.5311, 1.1696, 1.5171, 1.1675, 0.5143]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0017,Gradient_Exploding,"[-0.8, 1.2374, 1.4975, -0.7914, -0.4573, -0.0428, -0.0635, 1.0754, 1.3923, 0.058]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0018,Gradient_Exploding,"[-0.3286, -0.5441, 2.8487, -0.7786, -0.1628, 0.0409, 0.6032, 1.4709, 3.1516, -1.0022]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0019,Gradient_Exploding,"[0.2988, -0.4264, -0.0909, -0.8002, 1.1484, 0.1133, -0.7518, 0.6056, -0.6714, -1.4383]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0020,Gradient_Exploding,"[0.0679, 0.4847, -0.0838, -0.6574, -0.8464, -0.6435, 0.8528, 0.4948, -0.5634, 1.03]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0021,Gradient_Exploding,"[-0.3871, 0.0254, 1.7486, -1.0594, -1.9197, -0.0138, -0.0454, 1.3627, 1.5322, -0.6897]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0022,Gradient_Exploding,"[-0.1313, -0.2249, -1.1904, 0.2023, -0.65, 0.1687, 0.0769, -0.5173, -1.4021, 0.4419]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0023,Gradient_Exploding,"[0.1042, -0.754, -1.5734, -0.1034, -0.2807, -1.693, -0.0626, -0.3905, -2.1098, -0.0983]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0024,Gradient_Exploding,"[1.6334, 0.3026, 0.6342, -1.3799, -0.7543, -0.0641, -1.1463, 1.2817, -0.1331, 0.3288]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0025,Gradient_Exploding,"[0.5083, -2.0841, 0.2525, 0.5265, 1.7247, -0.2874, 3.9262, -0.3406, 0.6913, 0.2873]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0026,Gradient_Exploding,"[-1.0469, 1.1857, -0.6621, -0.8119, 0.719, 0.996, 0.5367, 0.4433, -1.4195, -0.7568]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0027,Gradient_Exploding,"[-0.385, 0.1413, -0.1524, 1.082, -2.1306, 0.7682, 0.383, -0.9015, 0.5512, 0.2154]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0028,Gradient_Exploding,"[-0.0955, -0.0806, 0.4712, 0.8441, -0.8331, 0.9154, -1.7762, -0.5262, 1.1944, -0.5495]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0029,Gradient_Exploding,"[-0.8988, -0.1891, 0.8776, -0.8558, 0.9217, -0.1275, -1.3303, 0.9403, 0.5448, 1.5112]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0030,Gradient_Exploding,"[0.4573, 0.7045, 0.431, 1.208, 0.7891, 0.0838, 1.4558, -0.826, 1.3941, 1.4105]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0031,Gradient_Exploding,"[1.5034, -0.221, 0.5442, -1.6116, 0.0269, 0.2084, 0.8774, 1.4379, -0.41, -2.0417]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0032,Gradient_Exploding,"[1.7325, 0.6381, -0.9678, 0.3188, 0.5008, -1.8011, 2.2313, -0.5426, -1.0332, -0.5427]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0033,Gradient_Exploding,"[-0.1295, -0.7069, 0.331, 0.696, 0.8556, 1.6495, 0.5799, -0.4511, 0.9103, 1.0706]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0034,Gradient_Exploding,"[0.2006, -1.0158, -0.8707, 1.9629, 0.0617, 0.4288, 1.1486, -1.8138, 0.2301, 0.6931]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0035,Gradient_Exploding,"[-0.5555, -1.2021, 0.5042, 0.9184, -0.3957, 0.3175, 0.2044, -0.575, 1.2886, -0.3329]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0036,Gradient_Exploding,"[1.9542, -1.0589, 1.1761, -0.0051, 1.4817, 1.9626, -0.5057, 0.3571, 1.5199, 0.0037]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0037,Gradient_Exploding,"[-0.2235, -0.0194, -1.407, 1.0619, -0.3032, 0.7999, -0.3493, -1.2622, -1.088, -1.6163]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0038,Gradient_Exploding,"[0.5609, 0.697, -2.6345, 1.2076, -0.3338, 1.1731, -0.2955, -1.7459, -2.5773, 0.3696]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0039,Gradient_Exploding,"[-1.3683, 0.9114, 0.3522, 0.7742, 0.1058, 1.2637, 1.9873, -0.5066, 0.9919, -0.8463]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0040,Gradient_Exploding,"[1.6699, -1.1959, -1.0928, 1.4935, 0.4446, 1.1966, 0.3947, -1.5092, -0.3823, -0.6098]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0041,Gradient_Exploding,"[0.5007, 0.0071, 2.004, -0.9471, -0.6603, 0.6988, 0.0498, 1.3506, 1.9408, 0.421]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0042,Gradient_Exploding,"[0.2578, 0.3342, 0.0893, 1.0982, -0.1553, -1.9078, -1.2418, -0.8418, 0.8755, -0.8604]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0043,Gradient_Exploding,"[-0.5467, 1.6735, 1.6079, -0.5492, 1.3405, -1.2996, -0.2717, 0.917, 1.7029, 0.8297]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0044,Gradient_Exploding,"[1.8466, -1.5255, 0.0813, -1.1341, -0.6919, -0.0456, -1.0701, 0.9213, -0.6793, 0.2433]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0045,Gradient_Exploding,"[1.1941, -0.4644, 0.0662, -0.5673, 0.4621, 0.7834, -0.9812, 0.4685, -0.3067, -0.2515]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0046,Gradient_Exploding,"[0.0859, -0.2298, 2.7768, -1.5438, -0.8514, 0.1752, -2.2193, 2.0545, 2.529, 2.9853]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0047,Gradient_Exploding,"[1.0827, -0.0936, 0.5943, 0.8675, 1.3258, -1.2872, -0.4711, -0.5077, 1.3701, -1.3971]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0048,Gradient_Exploding,"[0.3676, 0.6255, 0.4542, 0.8006, 0.8852, -0.5924, -2.1, -0.4969, 1.1423, 0.1235]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0049,Gradient_Exploding,"[1.1895, 0.5974, -1.5701, 0.2744, 0.7012, -0.2976, -1.2276, -0.6883, -1.8441, 1.3757]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0050,Gradient_Exploding,"[-0.7096, -0.217, 1.9494, 0.1897, -0.3085, 2.4267, -1.2585, 0.4352, 2.6565, 0.433]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0051,Gradient_Exploding,"[-1.3793, -0.0331, 1.4895, -1.8693, 1.7946, -0.5176, -0.7309, 1.9255, 0.6363, 0.2238]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0052,Gradient_Exploding,"[-0.1394, 0.0005, -0.5298, -0.7221, 0.6012, -1.4439, -0.4502, 0.4121, -1.1859, -2.2962]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0053,Gradient_Exploding,"[-0.0332, -0.1285, -1.5068, 1.7196, -1.8818, -0.5487, -0.2081, -1.8123, -0.7622, 0.0928]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0054,Gradient_Exploding,"[0.893, -0.4097, 0.0446, 1.3021, 0.7123, 2.2817, 1.8296, -1.0164, 0.9586, -0.6176]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0055,Gradient_Exploding,"[-0.134, -0.7849, -1.3711, 1.4005, 0.6483, -0.1209, 0.0147, -1.5192, -0.8072, 0.4195]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0056,Gradient_Exploding,"[-0.1671, 1.2065, 0.1808, -1.6848, -0.8169, 0.3687, 0.1467, 1.3868, -0.9315, -0.3933]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0057,Gradient_Exploding,"[2.0565, 0.4736, 0.5028, -1.611, -0.9264, 0.5555, -1.1309, 1.4251, -0.4632, -0.9187]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0058,Gradient_Exploding,"[-3.0195, 1.8005, 1.2812, -1.015, 1.2389, 0.2097, 0.1838, 1.1873, 0.9575, -0.4916]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0059,Gradient_Exploding,"[0.5436, 0.7717, -0.3913, -1.0757, -2.8485, 1.1488, -0.3706, 0.7333, -1.2512, -1.7397]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0060,Gradient_Exploding,"[2.1709, 0.1232, -1.0462, 0.3607, 0.5515, 0.0436, -0.1759, -0.5993, -1.1057, 1.6951]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0061,Gradient_Exploding,"[0.8205, 1.0667, 0.0147, -1.3368, 1.1693, 1.3822, 0.5073, 1.0616, -0.9058, 0.6487]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0062,Gradient_Exploding,"[-0.4368, 1.7496, 1.434, -0.207, 1.3815, -1.2923, -1.6066, 0.5942, 1.7144, 0.6897]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0063,Gradient_Exploding,"[-0.0469, 0.0768, 0.6822, -1.6285, -1.283, 0.9963, 0.477, 1.4927, -0.2429, -0.4938]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0064,Gradient_Exploding,"[-0.733, 0.7752, 0.0449, 0.6979, 0.553, 0.234, 1.8181, -0.5385, 0.541, -0.2485]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0065,Gradient_Exploding,"[0.1223, 0.0489, 0.1109, -1.0314, 0.0406, -0.702, 0.5433, 0.849, -0.5698, -0.6629]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0066,Gradient_Exploding,"[1.6672, 0.7356, 0.2185, 1.024, 1.7818, -1.6566, 1.5232, -0.7442, 0.9915, -0.5244]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0067,Gradient_Exploding,"[0.5112, -0.1374, -2.0175, 0.693, 0.9529, 1.6123, 1.3737, -1.1537, -2.134, 1.3149]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0068,Gradient_Exploding,"[0.1606, 0.4369, -1.0887, -0.6587, 1.1906, 0.9496, 0.003, 0.1942, -1.8661, -1.4849]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0069,Gradient_Exploding,"[0.195, -1.1306, 0.9334, -0.5818, 0.6227, 0.6295, -0.7583, 0.7403, 0.8067, -0.8043]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0070,Gradient_Exploding,"[-0.2472, -1.0016, -1.2444, -0.5943, -0.2811, 1.7977, -0.682, 0.0964, -2.0232, 0.6408]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0071,Gradient_Exploding,"[0.0156, -0.9078, 0.0976, 1.3665, 1.5193, 0.5108, 0.9023, -1.0514, 1.0718, 1.0306]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0072,Gradient_Exploding,"[-1.2504, 0.8823, 1.4655, -0.9689, -0.4521, -0.47, 0.6045, 1.2062, 1.2281, 0.2659]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0073,Gradient_Exploding,"[-0.9037, -1.179, -0.772, 0.5423, 1.1877, -0.4646, 0.3244, -0.6606, -0.6249, 0.2012]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0074,Gradient_Exploding,"[0.8701, 0.1504, -0.0493, -1.0418, 0.365, 2.4034, 0.4957, 0.8091, -0.7846, -0.0576]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0075,Gradient_Exploding,"[-0.8245, -0.529, -0.2098, 1.2559, -1.0566, 1.2231, -0.6086, -1.0563, 0.5971, -0.2589]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0076,Gradient_Exploding,"[0.7408, -0.2286, 1.0974, -1.3862, -0.9943, -2.5623, -0.5132, 1.4257, 0.4625, -0.191]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0077,Gradient_Exploding,"[1.5285, 0.5383, -1.794, 0.9486, 1.0725, -0.365, 0.5078, -1.2888, -1.6677, -0.8392]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0078,Gradient_Exploding,"[1.2005, -1.9671, -0.6158, 1.1108, -1.1172, -0.1858, 0.1404, -1.0633, -0.0292, 0.31]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0079,Gradient_Exploding,"[0.181, 0.3997, -4.6362, 2.1216, -0.6514, -0.5286, -1.2968, -3.0697, -4.5379, 0.5864]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0080,Gradient_Exploding,"[-0.5836, -1.5193, 0.8414, 0.8087, -2.8322, -0.4512, 1.0384, -0.387, 1.6494, 0.5517]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0081,Gradient_Exploding,"[-0.9564, 1.4841, 1.5456, -1.3936, 0.3556, -0.3131, 0.4724, 1.5661, 1.0381, -0.0007]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0082,Gradient_Exploding,"[0.0701, -0.9274, -1.1379, 0.5054, 0.2384, 0.9752, 1.1619, -0.7413, -1.1243, 0.5011]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0083,Gradient_Exploding,"[-0.6267, 0.9531, -0.8192, 0.0523, 0.5131, 0.7251, 0.8624, -0.2873, -1.0251, 0.5162]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0084,Gradient_Exploding,"[0.0012, 0.6592, -2.4544, 2.8315, 0.9376, -1.6076, -0.8171, -2.9762, -1.2204, -0.7627]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0085,Gradient_Exploding,"[1.642, -0.6882, -2.2899, -0.4861, 2.2524, 0.9818, 1.0098, -0.3029, -3.3027, -0.3248]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0086,Gradient_Exploding,"[-1.0852, 2.9491, 1.036, -0.488, 1.2447, -1.3511, -0.8254, 0.6969, 1.0044, -1.3225]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0087,Gradient_Exploding,"[-0.4135, -0.9638, 1.8621, -1.9512, -0.9572, 0.3438, 0.2603, 2.1021, 1.0623, -0.0487]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0088,Gradient_Exploding,"[-0.6691, -0.6056, -0.4768, -0.7027, 1.826, 0.6779, 1.0399, 0.4126, -1.1038, -0.4879]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0089,Gradient_Exploding,"[-1.4789, -0.0153, 0.0684, -0.7535, 0.5793, 0.1196, 0.1834, 0.6164, -0.4327, -0.9731]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0090,Gradient_Exploding,"[1.2172, 0.9983, -1.8119, 1.3461, -0.4316, 0.4037, 1.5213, -1.6085, -1.4158, -0.0242]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0091,Gradient_Exploding,"[-0.1073, -1.5709, -1.6514, 1.2402, -1.1268, -1.1939, 0.4477, -1.4766, -1.2812, 0.1429]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0092,Gradient_Exploding,"[0.7442, -0.6494, 1.8373, -1.0258, 1.3213, 1.4196, -0.1812, 1.3628, 1.6704, -0.6004]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0093,Gradient_Exploding,"[-1.0448, 2.0562, -2.2278, 0.5918, -1.1032, -0.2213, -1.9664, -1.1368, -2.4764, -0.2768]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0094,Gradient_Exploding,"[0.0208, -0.1829, -1.244, 1.68, 1.3749, -0.646, -0.728, -1.7021, -0.4491, -0.7992]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0095,Gradient_Exploding,"[1.5505, 0.9843, -1.8613, 0.1866, -0.214, -0.0495, -0.9984, -0.7063, -2.282, 0.6748]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0096,Gradient_Exploding,"[1.2131, 2.3193, 0.0011, -1.4026, 0.3933, 0.192, 0.1417, 1.1096, -0.969, -0.3091]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0097,Gradient_Exploding,"[-0.7727, 0.9004, -0.0346, 1.1486, 0.4512, 1.1835, 0.8302, -0.9188, 0.7498, -1.178]",1,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0098,Gradient_Exploding,"[1.3843, 0.5948, 0.7396, -1.2479, 0.8534, 0.7589, 0.5639, 1.2089, 0.0947, 0.2812]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GEX_0099,Gradient_Exploding,"[-0.9219, 0.2073, 2.0764, -1.7494, 0.0693, -0.7217, -1.004, 2.0068, 1.4794, 0.1768]",0,0.5,0.8833,0.8907,0.8833,0.8831,Training loss diverges wildly. Model fails to converge.,Learning rate=10 too large; no gradient clipping.,"Gradient clipping (max_norm=1.0), reduce LR, batch normalization, Xavier init.",High,"{'lr': 10.0, 'max_loss': 56.0}"
GVN_0000,Gradient_Vanishing,"[-0.0061, 0.0818, -0.0234, -1.6573, -0.0989, 0.9191, 0.8385, 1.3037, -1.1769, -0.2903]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0001,Gradient_Vanishing,"[-0.3191, 1.076, -2.0025, 0.4319, 0.0213, 1.9012, -0.796, -0.9427, -2.2952, -0.0607]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0002,Gradient_Vanishing,"[-1.2097, 1.5308, 0.9901, -1.7422, 1.2188, -0.2134, 0.5999, 1.6751, 0.0773, 1.4907]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0003,Gradient_Vanishing,"[0.0887, -0.6177, 0.6994, -0.4948, -0.3161, 0.6158, 0.1973, 0.6013, 0.5636, 1.2039]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0004,Gradient_Vanishing,"[0.3525, -1.8206, 0.1471, 1.1043, 0.2701, -1.9123, -0.5704, -0.8292, 0.9545, -0.0686]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0005,Gradient_Vanishing,"[-1.1618, -0.3009, 0.2424, 1.4912, -1.2095, 0.389, -0.2831, -1.1066, 1.3457, 0.2515]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0006,Gradient_Vanishing,"[-0.3216, 0.3819, -0.3235, -1.2497, 0.43, 1.0303, 2.0767, 0.8912, -1.2836, 0.2388]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0007,Gradient_Vanishing,"[0.2956, 1.4769, 0.9114, -0.0142, -1.1678, 0.2168, -1.5166, 0.2848, 1.1708, -1.0973]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0008,Gradient_Vanishing,"[-0.8096, -0.4738, -1.0568, 0.9007, -0.0145, 0.5463, 0.4241, -1.0296, -0.7458, 0.0064]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0009,Gradient_Vanishing,"[0.1896, -2.7032, -2.2516, 1.5965, 0.6779, -0.6541, 1.001, -1.9385, -1.8121, -1.8306]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0010,Gradient_Vanishing,"[0.492, -2.1533, 1.6761, -0.6712, 1.0972, -0.4788, -0.526, 1.034, 1.7068, -0.8628]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0011,Gradient_Vanishing,"[0.0727, -0.0892, 0.2943, 1.2699, -0.0376, -1.7312, -0.4115, -0.916, 1.2598, 1.4949]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0012,Gradient_Vanishing,"[0.6726, -0.1326, -1.5816, 0.438, -0.9745, 1.1071, 1.8999, -0.8211, -1.7458, -0.1204]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0013,Gradient_Vanishing,"[-0.6752, -0.7924, -0.9148, -0.0792, -0.308, -1.8936, -0.1445, -0.212, -1.2399, 0.2133]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0014,Gradient_Vanishing,"[-0.4931, -1.2565, 1.3133, -0.6672, 0.8132, -0.279, 1.4436, 0.9219, 1.2396, -0.2798]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0015,Gradient_Vanishing,"[-1.5566, 1.5008, -1.084, -0.3345, 0.8502, -0.3487, -0.4281, -0.0608, -1.6356, -0.3493]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0016,Gradient_Vanishing,"[1.0473, -0.2294, 1.6012, -1.3105, -0.0435, -1.5311, 1.1696, 1.5171, 1.1675, 0.5143]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0017,Gradient_Vanishing,"[-0.8, 1.2374, 1.4975, -0.7914, -0.4573, -0.0428, -0.0635, 1.0754, 1.3923, 0.058]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0018,Gradient_Vanishing,"[-0.3286, -0.5441, 2.8487, -0.7786, -0.1628, 0.0409, 0.6032, 1.4709, 3.1516, -1.0022]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0019,Gradient_Vanishing,"[0.2988, -0.4264, -0.0909, -0.8002, 1.1484, 0.1133, -0.7518, 0.6056, -0.6714, -1.4383]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0020,Gradient_Vanishing,"[0.0679, 0.4847, -0.0838, -0.6574, -0.8464, -0.6435, 0.8528, 0.4948, -0.5634, 1.03]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0021,Gradient_Vanishing,"[-0.3871, 0.0254, 1.7486, -1.0594, -1.9197, -0.0138, -0.0454, 1.3627, 1.5322, -0.6897]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0022,Gradient_Vanishing,"[-0.1313, -0.2249, -1.1904, 0.2023, -0.65, 0.1687, 0.0769, -0.5173, -1.4021, 0.4419]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0023,Gradient_Vanishing,"[0.1042, -0.754, -1.5734, -0.1034, -0.2807, -1.693, -0.0626, -0.3905, -2.1098, -0.0983]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0024,Gradient_Vanishing,"[1.6334, 0.3026, 0.6342, -1.3799, -0.7543, -0.0641, -1.1463, 1.2817, -0.1331, 0.3288]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0025,Gradient_Vanishing,"[0.5083, -2.0841, 0.2525, 0.5265, 1.7247, -0.2874, 3.9262, -0.3406, 0.6913, 0.2873]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0026,Gradient_Vanishing,"[-1.0469, 1.1857, -0.6621, -0.8119, 0.719, 0.996, 0.5367, 0.4433, -1.4195, -0.7568]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0027,Gradient_Vanishing,"[-0.385, 0.1413, -0.1524, 1.082, -2.1306, 0.7682, 0.383, -0.9015, 0.5512, 0.2154]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0028,Gradient_Vanishing,"[-0.0955, -0.0806, 0.4712, 0.8441, -0.8331, 0.9154, -1.7762, -0.5262, 1.1944, -0.5495]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0029,Gradient_Vanishing,"[-0.8988, -0.1891, 0.8776, -0.8558, 0.9217, -0.1275, -1.3303, 0.9403, 0.5448, 1.5112]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0030,Gradient_Vanishing,"[0.4573, 0.7045, 0.431, 1.208, 0.7891, 0.0838, 1.4558, -0.826, 1.3941, 1.4105]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0031,Gradient_Vanishing,"[1.5034, -0.221, 0.5442, -1.6116, 0.0269, 0.2084, 0.8774, 1.4379, -0.41, -2.0417]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0032,Gradient_Vanishing,"[1.7325, 0.6381, -0.9678, 0.3188, 0.5008, -1.8011, 2.2313, -0.5426, -1.0332, -0.5427]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0033,Gradient_Vanishing,"[-0.1295, -0.7069, 0.331, 0.696, 0.8556, 1.6495, 0.5799, -0.4511, 0.9103, 1.0706]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0034,Gradient_Vanishing,"[0.2006, -1.0158, -0.8707, 1.9629, 0.0617, 0.4288, 1.1486, -1.8138, 0.2301, 0.6931]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0035,Gradient_Vanishing,"[-0.5555, -1.2021, 0.5042, 0.9184, -0.3957, 0.3175, 0.2044, -0.575, 1.2886, -0.3329]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0036,Gradient_Vanishing,"[1.9542, -1.0589, 1.1761, -0.0051, 1.4817, 1.9626, -0.5057, 0.3571, 1.5199, 0.0037]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0037,Gradient_Vanishing,"[-0.2235, -0.0194, -1.407, 1.0619, -0.3032, 0.7999, -0.3493, -1.2622, -1.088, -1.6163]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0038,Gradient_Vanishing,"[0.5609, 0.697, -2.6345, 1.2076, -0.3338, 1.1731, -0.2955, -1.7459, -2.5773, 0.3696]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0039,Gradient_Vanishing,"[-1.3683, 0.9114, 0.3522, 0.7742, 0.1058, 1.2637, 1.9873, -0.5066, 0.9919, -0.8463]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0040,Gradient_Vanishing,"[1.6699, -1.1959, -1.0928, 1.4935, 0.4446, 1.1966, 0.3947, -1.5092, -0.3823, -0.6098]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0041,Gradient_Vanishing,"[0.5007, 0.0071, 2.004, -0.9471, -0.6603, 0.6988, 0.0498, 1.3506, 1.9408, 0.421]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0042,Gradient_Vanishing,"[0.2578, 0.3342, 0.0893, 1.0982, -0.1553, -1.9078, -1.2418, -0.8418, 0.8755, -0.8604]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0043,Gradient_Vanishing,"[-0.5467, 1.6735, 1.6079, -0.5492, 1.3405, -1.2996, -0.2717, 0.917, 1.7029, 0.8297]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0044,Gradient_Vanishing,"[1.8466, -1.5255, 0.0813, -1.1341, -0.6919, -0.0456, -1.0701, 0.9213, -0.6793, 0.2433]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0045,Gradient_Vanishing,"[1.1941, -0.4644, 0.0662, -0.5673, 0.4621, 0.7834, -0.9812, 0.4685, -0.3067, -0.2515]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0046,Gradient_Vanishing,"[0.0859, -0.2298, 2.7768, -1.5438, -0.8514, 0.1752, -2.2193, 2.0545, 2.529, 2.9853]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0047,Gradient_Vanishing,"[1.0827, -0.0936, 0.5943, 0.8675, 1.3258, -1.2872, -0.4711, -0.5077, 1.3701, -1.3971]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0048,Gradient_Vanishing,"[0.3676, 0.6255, 0.4542, 0.8006, 0.8852, -0.5924, -2.1, -0.4969, 1.1423, 0.1235]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0049,Gradient_Vanishing,"[1.1895, 0.5974, -1.5701, 0.2744, 0.7012, -0.2976, -1.2276, -0.6883, -1.8441, 1.3757]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0050,Gradient_Vanishing,"[-0.7096, -0.217, 1.9494, 0.1897, -0.3085, 2.4267, -1.2585, 0.4352, 2.6565, 0.433]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0051,Gradient_Vanishing,"[-1.3793, -0.0331, 1.4895, -1.8693, 1.7946, -0.5176, -0.7309, 1.9255, 0.6363, 0.2238]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0052,Gradient_Vanishing,"[-0.1394, 0.0005, -0.5298, -0.7221, 0.6012, -1.4439, -0.4502, 0.4121, -1.1859, -2.2962]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0053,Gradient_Vanishing,"[-0.0332, -0.1285, -1.5068, 1.7196, -1.8818, -0.5487, -0.2081, -1.8123, -0.7622, 0.0928]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0054,Gradient_Vanishing,"[0.893, -0.4097, 0.0446, 1.3021, 0.7123, 2.2817, 1.8296, -1.0164, 0.9586, -0.6176]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0055,Gradient_Vanishing,"[-0.134, -0.7849, -1.3711, 1.4005, 0.6483, -0.1209, 0.0147, -1.5192, -0.8072, 0.4195]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0056,Gradient_Vanishing,"[-0.1671, 1.2065, 0.1808, -1.6848, -0.8169, 0.3687, 0.1467, 1.3868, -0.9315, -0.3933]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0057,Gradient_Vanishing,"[2.0565, 0.4736, 0.5028, -1.611, -0.9264, 0.5555, -1.1309, 1.4251, -0.4632, -0.9187]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0058,Gradient_Vanishing,"[-3.0195, 1.8005, 1.2812, -1.015, 1.2389, 0.2097, 0.1838, 1.1873, 0.9575, -0.4916]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0059,Gradient_Vanishing,"[0.5436, 0.7717, -0.3913, -1.0757, -2.8485, 1.1488, -0.3706, 0.7333, -1.2512, -1.7397]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0060,Gradient_Vanishing,"[2.1709, 0.1232, -1.0462, 0.3607, 0.5515, 0.0436, -0.1759, -0.5993, -1.1057, 1.6951]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0061,Gradient_Vanishing,"[0.8205, 1.0667, 0.0147, -1.3368, 1.1693, 1.3822, 0.5073, 1.0616, -0.9058, 0.6487]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0062,Gradient_Vanishing,"[-0.4368, 1.7496, 1.434, -0.207, 1.3815, -1.2923, -1.6066, 0.5942, 1.7144, 0.6897]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0063,Gradient_Vanishing,"[-0.0469, 0.0768, 0.6822, -1.6285, -1.283, 0.9963, 0.477, 1.4927, -0.2429, -0.4938]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0064,Gradient_Vanishing,"[-0.733, 0.7752, 0.0449, 0.6979, 0.553, 0.234, 1.8181, -0.5385, 0.541, -0.2485]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0065,Gradient_Vanishing,"[0.1223, 0.0489, 0.1109, -1.0314, 0.0406, -0.702, 0.5433, 0.849, -0.5698, -0.6629]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0066,Gradient_Vanishing,"[1.6672, 0.7356, 0.2185, 1.024, 1.7818, -1.6566, 1.5232, -0.7442, 0.9915, -0.5244]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0067,Gradient_Vanishing,"[0.5112, -0.1374, -2.0175, 0.693, 0.9529, 1.6123, 1.3737, -1.1537, -2.134, 1.3149]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0068,Gradient_Vanishing,"[0.1606, 0.4369, -1.0887, -0.6587, 1.1906, 0.9496, 0.003, 0.1942, -1.8661, -1.4849]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0069,Gradient_Vanishing,"[0.195, -1.1306, 0.9334, -0.5818, 0.6227, 0.6295, -0.7583, 0.7403, 0.8067, -0.8043]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0070,Gradient_Vanishing,"[-0.2472, -1.0016, -1.2444, -0.5943, -0.2811, 1.7977, -0.682, 0.0964, -2.0232, 0.6408]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0071,Gradient_Vanishing,"[0.0156, -0.9078, 0.0976, 1.3665, 1.5193, 0.5108, 0.9023, -1.0514, 1.0718, 1.0306]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0072,Gradient_Vanishing,"[-1.2504, 0.8823, 1.4655, -0.9689, -0.4521, -0.47, 0.6045, 1.2062, 1.2281, 0.2659]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0073,Gradient_Vanishing,"[-0.9037, -1.179, -0.772, 0.5423, 1.1877, -0.4646, 0.3244, -0.6606, -0.6249, 0.2012]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0074,Gradient_Vanishing,"[0.8701, 0.1504, -0.0493, -1.0418, 0.365, 2.4034, 0.4957, 0.8091, -0.7846, -0.0576]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0075,Gradient_Vanishing,"[-0.8245, -0.529, -0.2098, 1.2559, -1.0566, 1.2231, -0.6086, -1.0563, 0.5971, -0.2589]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0076,Gradient_Vanishing,"[0.7408, -0.2286, 1.0974, -1.3862, -0.9943, -2.5623, -0.5132, 1.4257, 0.4625, -0.191]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0077,Gradient_Vanishing,"[1.5285, 0.5383, -1.794, 0.9486, 1.0725, -0.365, 0.5078, -1.2888, -1.6677, -0.8392]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0078,Gradient_Vanishing,"[1.2005, -1.9671, -0.6158, 1.1108, -1.1172, -0.1858, 0.1404, -1.0633, -0.0292, 0.31]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0079,Gradient_Vanishing,"[0.181, 0.3997, -4.6362, 2.1216, -0.6514, -0.5286, -1.2968, -3.0697, -4.5379, 0.5864]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0080,Gradient_Vanishing,"[-0.5836, -1.5193, 0.8414, 0.8087, -2.8322, -0.4512, 1.0384, -0.387, 1.6494, 0.5517]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0081,Gradient_Vanishing,"[-0.9564, 1.4841, 1.5456, -1.3936, 0.3556, -0.3131, 0.4724, 1.5661, 1.0381, -0.0007]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0082,Gradient_Vanishing,"[0.0701, -0.9274, -1.1379, 0.5054, 0.2384, 0.9752, 1.1619, -0.7413, -1.1243, 0.5011]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0083,Gradient_Vanishing,"[-0.6267, 0.9531, -0.8192, 0.0523, 0.5131, 0.7251, 0.8624, -0.2873, -1.0251, 0.5162]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0084,Gradient_Vanishing,"[0.0012, 0.6592, -2.4544, 2.8315, 0.9376, -1.6076, -0.8171, -2.9762, -1.2204, -0.7627]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0085,Gradient_Vanishing,"[1.642, -0.6882, -2.2899, -0.4861, 2.2524, 0.9818, 1.0098, -0.3029, -3.3027, -0.3248]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0086,Gradient_Vanishing,"[-1.0852, 2.9491, 1.036, -0.488, 1.2447, -1.3511, -0.8254, 0.6969, 1.0044, -1.3225]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0087,Gradient_Vanishing,"[-0.4135, -0.9638, 1.8621, -1.9512, -0.9572, 0.3438, 0.2603, 2.1021, 1.0623, -0.0487]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0088,Gradient_Vanishing,"[-0.6691, -0.6056, -0.4768, -0.7027, 1.826, 0.6779, 1.0399, 0.4126, -1.1038, -0.4879]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0089,Gradient_Vanishing,"[-1.4789, -0.0153, 0.0684, -0.7535, 0.5793, 0.1196, 0.1834, 0.6164, -0.4327, -0.9731]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0090,Gradient_Vanishing,"[1.2172, 0.9983, -1.8119, 1.3461, -0.4316, 0.4037, 1.5213, -1.6085, -1.4158, -0.0242]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0091,Gradient_Vanishing,"[-0.1073, -1.5709, -1.6514, 1.2402, -1.1268, -1.1939, 0.4477, -1.4766, -1.2812, 0.1429]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0092,Gradient_Vanishing,"[0.7442, -0.6494, 1.8373, -1.0258, 1.3213, 1.4196, -0.1812, 1.3628, 1.6704, -0.6004]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0093,Gradient_Vanishing,"[-1.0448, 2.0562, -2.2278, 0.5918, -1.1032, -0.2213, -1.9664, -1.1368, -2.4764, -0.2768]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0094,Gradient_Vanishing,"[0.0208, -0.1829, -1.244, 1.68, 1.3749, -0.646, -0.728, -1.7021, -0.4491, -0.7992]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0095,Gradient_Vanishing,"[1.5505, 0.9843, -1.8613, 0.1866, -0.214, -0.0495, -0.9984, -0.7063, -2.282, 0.6748]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0096,Gradient_Vanishing,"[1.2131, 2.3193, 0.0011, -1.4026, 0.3933, 0.192, 0.1417, 1.1096, -0.969, -0.3091]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0097,Gradient_Vanishing,"[-0.7727, 0.9004, -0.0346, 1.1486, 0.4512, 1.1835, 0.8302, -0.9188, 0.7498, -1.178]",1,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0098,Gradient_Vanishing,"[1.3843, 0.5948, 0.7396, -1.2479, 0.8534, 0.7589, 0.5639, 1.2089, 0.0947, 0.2812]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
GVN_0099,Gradient_Vanishing,"[-0.9219, 0.2073, 2.0764, -1.7494, 0.0693, -0.7217, -1.004, 2.0068, 1.4794, 0.1768]",0,0.5,0.4833,0.2336,0.4833,0.315,Gradients near-zero in early layers; model stops learning.,Sigmoid activations in 4-layer deep net shrink gradients exponentially.,"Use ReLU/LeakyReLU, residual connections (ResNet), batch normalization, LSTM for sequences.",High,"{'activation': 'sigmoid', 'layers': 4}"
DRF_0000,Model_Drift,"[-0.9495, 4.3241, 0.2055, 3.5928, 0.9577, 0.6023, 0.4924, -2.0817, -1.0122, -3.9204]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0001,Model_Drift,"[2.8803, 0.2858, 2.1632, -0.0163, 4.3409, 1.839, 0.1932, 5.2937, -1.4867, 6.8457]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0002,Model_Drift,"[2.3449, 2.2941, 0.5587, 2.4529, 3.1765, 0.7992, 2.1794, 1.9584, 2.6264, 0.9627]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0003,Model_Drift,"[1.1595, 2.3766, 3.2964, -0.7938, 0.2446, 4.246, 4.9879, 1.5982, 1.9281, 3.2071]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0004,Model_Drift,"[1.3618, 3.1609, -0.0183, 2.2317, 4.5589, -0.3047, -1.6604, 2.1221, 0.6197, -0.2873]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0005,Model_Drift,"[2.489, 2.4396, 2.5945, -0.572, 2.9654, 2.0117, 2.8042, 1.8168, 0.5599, 3.9992]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0006,Model_Drift,"[-1.2941, -2.3462, 7.2115, 2.8574, -5.6109, 5.7613, 3.1876, 1.5987, -2.96, 7.5578]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0007,Model_Drift,"[0.2084, 0.3107, 2.2845, 0.4666, 1.1605, 3.0037, -0.2166, 4.6189, -0.2476, 3.8412]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0008,Model_Drift,"[0.8058, 0.9238, 3.5961, -0.8175, 1.3815, 3.059, 2.5206, 4.2839, -0.511, 5.8586]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0009,Model_Drift,"[0.1977, 3.7363, 2.3719, 1.5414, 1.8432, -0.1343, 1.8993, -0.6023, -0.7178, 0.5296]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0010,Model_Drift,"[2.534, 2.751, 2.8838, -1.2186, 3.5928, -0.013, 3.9426, 1.3127, -0.3688, 4.7533]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0011,Model_Drift,"[2.8198, -0.2957, 1.7089, 0.6485, 0.8904, 2.0331, 2.5912, 1.8253, 2.3236, 3.4947]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0012,Model_Drift,"[3.5002, 1.2898, 1.5338, 2.1624, 5.7657, -2.7305, 6.3754, 6.1384, 3.0466, 6.4541]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0013,Model_Drift,"[-1.481, 3.709, -1.6671, 4.9285, -0.6525, 2.171, 4.7255, -1.4833, -0.1739, -5.518]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0014,Model_Drift,"[-0.2819, 0.6846, -1.5303, 5.1226, 1.1723, -0.0529, 0.4126, 4.2533, 5.4965, -2.761]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0015,Model_Drift,"[1.0025, -2.6716, 4.5145, 1.7854, -4.5844, 0.0725, 3.1064, -1.4527, 3.938, 3.3256]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0016,Model_Drift,"[1.2733, -0.6107, -0.4762, 0.0495, 0.1093, 0.8716, -2.4991, -0.3069, 0.59, -0.8788]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0017,Model_Drift,"[2.7733, 2.4991, 0.7959, -2.4387, 4.2224, -0.4679, 3.361, 1.6566, 0.6166, 2.962]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0018,Model_Drift,"[1.6043, -0.3973, 3.4237, 3.4707, -2.5861, 4.1698, 3.1003, -0.4637, 2.5205, 2.3731]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0019,Model_Drift,"[-0.8222, 2.1282, 0.6844, 2.4959, 1.6641, -2.8597, -0.6625, 2.48, 5.2776, -1.9939]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0020,Model_Drift,"[1.6204, 2.4489, -1.1705, 4.2012, 3.0906, 3.3956, -0.5092, 2.4685, 1.3181, -1.4842]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0021,Model_Drift,"[3.8853, 2.5274, 0.3921, -1.6185, 5.638, -1.5033, 2.5121, 1.3567, 0.1237, 3.2858]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0022,Model_Drift,"[1.5254, 2.3089, 1.3518, 2.4099, 0.2467, 4.9991, 3.5391, 0.5018, 1.6864, 0.3783]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0023,Model_Drift,"[3.608, 1.1481, 2.9835, 3.1935, 4.5112, -1.8487, 2.454, 3.2047, 1.6516, 5.9388]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0024,Model_Drift,"[2.9183, -1.0312, 1.8516, 0.9968, 0.2622, 0.3732, 3.2071, 1.0679, 2.9397, 3.5428]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0025,Model_Drift,"[1.3858, 3.2264, 0.2196, 1.7528, 2.9812, 0.9064, 2.291, 1.6104, 2.4611, -0.4446]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0026,Model_Drift,"[-1.1646, 0.9733, 2.9141, 3.1776, 0.5836, -0.1212, 3.5041, 5.0607, 1.8191, 3.3009]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0027,Model_Drift,"[1.3593, 0.0647, 1.5243, -0.0824, 1.3384, 0.9147, 0.8296, 2.2211, -0.3017, 3.1991]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0028,Model_Drift,"[3.4647, 1.2695, 4.0589, 3.2575, 2.2947, 2.2169, 2.4305, 1.6676, 1.2141, 5.6436]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0029,Model_Drift,"[-0.2169, 0.4535, 1.6942, 4.8741, 1.738, 0.3648, 1.8203, 5.6688, 1.0531, 3.0999]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0030,Model_Drift,"[1.318, 0.897, 3.3593, -1.2098, -0.9698, 4.1696, 0.8897, -0.7215, -0.0819, 2.6863]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0031,Model_Drift,"[-2.3905, 2.5548, 1.122, -0.1229, -0.5354, 2.6471, 1.8671, 2.2654, 0.0579, -0.9843]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0032,Model_Drift,"[1.3149, 0.1886, 0.3231, -0.8217, 1.4542, 2.0444, 0.7721, 2.7232, 0.2044, 2.0308]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0033,Model_Drift,"[2.4298, 3.1899, -0.0758, 2.1927, 2.0269, 4.4316, 2.7819, 0.775, 3.6451, -1.1989]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0034,Model_Drift,"[1.3758, 3.4009, 0.5221, 1.3985, 5.297, -3.2436, 4.51, 3.7874, 1.827, 2.1197]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0035,Model_Drift,"[2.879, -1.2596, 1.2308, 1.382, 2.8378, 3.2909, -1.279, 4.6718, -2.1956, 5.7966]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0036,Model_Drift,"[2.2545, 2.487, -0.725, 2.3064, 2.9433, 2.2008, -1.4767, 0.64, 2.4937, -1.8446]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0037,Model_Drift,"[2.4911, 2.6705, -0.9018, 3.6805, 5.1061, 0.9734, -2.121, 2.447, 0.5793, -0.3179]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0038,Model_Drift,"[2.1771, -0.3936, 4.4895, -0.8196, -2.9127, 1.9659, -0.9776, -4.1781, 2.8712, 1.7036]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0039,Model_Drift,"[0.2362, 2.304, 0.6792, 0.4333, 1.4883, 0.2466, 0.1351, 0.3773, 0.5392, -0.6764]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0040,Model_Drift,"[-3.281, 3.4579, -0.6277, 1.6746, 0.2244, -0.7831, 2.2234, 0.9272, -0.9224, -3.8296]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0041,Model_Drift,"[0.4581, 1.9938, -0.346, 3.0413, -0.193, 4.2481, 3.5631, 0.5055, 0.7763, -1.608]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0042,Model_Drift,"[2.9465, -0.8555, 2.8217, 1.1115, 0.6625, 4.9916, 2.4011, 3.3107, 0.1015, 6.0808]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0043,Model_Drift,"[5.9764, 0.9998, 3.2185, 0.9843, 5.9428, -0.0229, 3.1041, 3.0209, 0.1097, 8.6463]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0044,Model_Drift,"[-0.3572, 4.637, -1.4062, 0.1738, 1.9915, 3.0743, 2.0774, 0.0926, 0.0588, -3.86]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0045,Model_Drift,"[1.5417, 0.8381, 1.3671, 3.5678, 2.4427, -0.3666, 1.1404, 3.1042, 1.8753, 2.3639]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0046,Model_Drift,"[1.5354, 1.5713, 1.1767, 1.4072, 1.1025, 0.0849, 5.2261, 0.8216, 2.0537, 1.4681]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0047,Model_Drift,"[-1.6184, 1.1832, 0.6894, 3.2083, 0.9804, 0.684, -0.502, 4.5835, 1.323, -0.0931]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0048,Model_Drift,"[1.0213, 0.5269, 0.4582, 2.0625, -0.0322, 2.7209, 2.357, 0.7977, -0.0254, 0.6371]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0049,Model_Drift,"[1.3732, 2.235, 2.8964, -1.4555, 2.8048, 1.0215, 2.9913, 2.6013, -0.8932, 4.7428]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0050,Model_Drift,"[0.7869, 4.4117, 0.6537, 3.5318, 1.9176, 2.8111, 4.6514, 0.3582, 1.2791, -1.2078]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0051,Model_Drift,"[5.9167, 0.3228, 1.3977, 0.0994, 4.7669, 1.2653, 3.0959, 1.1418, -2.1386, 6.805]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0052,Model_Drift,"[2.3185, -0.9458, 0.9964, 1.194, 1.2561, 4.2011, -0.0065, 3.8678, 0.3252, 3.8]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0053,Model_Drift,"[0.5535, 6.3784, 0.1879, 5.7977, 1.8753, 3.5666, 5.5807, -1.4064, 1.9687, -3.9661]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0054,Model_Drift,"[1.1229, 0.1339, 0.704, -3.0875, -0.5898, 3.1217, 2.4694, 0.5395, 0.5697, 1.345]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0055,Model_Drift,"[3.3093, -0.7562, 1.6459, -0.6662, 1.0982, -1.2635, 1.5645, 0.3792, 3.8859, 2.9327]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0056,Model_Drift,"[2.1677, 3.8224, 0.0612, -0.2149, 2.8751, 2.413, 3.0778, -0.4934, 0.5289, -0.4849]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0057,Model_Drift,"[0.8757, 1.1732, 2.4263, 3.9952, 1.9509, -1.1281, 2.6905, 3.9473, 4.0612, 2.7318]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0058,Model_Drift,"[-0.2499, 1.7349, -1.9264, 2.434, 1.5372, 2.017, -1.3967, 2.7385, 2.7687, -3.5264]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0059,Model_Drift,"[0.9368, 0.5898, 3.3733, -0.6217, 0.692, 1.5725, 2.8534, 2.0097, -1.5532, 5.1419]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0060,Model_Drift,"[-1.0192, 3.404, -0.0987, 3.5539, -0.2739, 1.7851, 0.2194, -2.321, -0.7913, -4.3418]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0061,Model_Drift,"[1.6209, 1.1541, -1.1466, 6.5331, 3.8901, -1.2017, -3.1265, 3.8768, 4.8259, -1.7201]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0062,Model_Drift,"[1.0051, 1.182, 2.3433, -0.521, 1.3823, 1.1477, 0.879, 1.8263, 0.2679, 2.9449]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0063,Model_Drift,"[-3.2312, 3.3378, -0.7907, 2.2344, -1.2603, 0.2405, -0.3475, 0.6296, 5.9266, -7.1843]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0064,Model_Drift,"[-2.1533, 2.5342, -1.4531, 1.0447, -0.4041, 3.5152, 1.4732, 2.2587, 1.5008, -4.1067]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0065,Model_Drift,"[1.4222, -1.9062, 5.1842, 1.1047, -4.952, 2.3653, 0.9901, -3.5923, 2.4332, 2.8408]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0066,Model_Drift,"[1.885, -0.9324, 2.214, 1.9303, 0.3781, -1.7525, 2.665, 1.2684, 2.2105, 3.4841]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0067,Model_Drift,"[3.0272, 0.1576, 2.246, 2.2815, 0.2919, 0.4624, 4.3209, -0.375, 2.6898, 2.8853]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0068,Model_Drift,"[3.6962, 1.6167, 2.5168, 2.0502, 1.5215, 4.5202, 1.6415, -0.1299, 1.2561, 2.9529]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0069,Model_Drift,"[1.0202, -0.6554, 2.5495, -0.5676, -0.0969, 0.4539, 0.91, 1.4338, 0.2831, 3.6354]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0070,Model_Drift,"[1.6622, 3.3404, -3.7726, 3.6806, 4.2947, -0.3598, -4.184, 0.6477, 5.385, -6.8905]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0071,Model_Drift,"[3.0826, -2.7832, 2.0707, 0.2214, -0.357, 0.4259, 2.775, 0.3674, -1.9585, 5.8787]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0072,Model_Drift,"[1.5474, 0.5008, 1.2115, -0.5177, 0.0789, 1.7401, 4.0697, 0.7697, 1.2934, 1.9885]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0073,Model_Drift,"[1.4036, 3.067, 2.0957, 1.2473, 1.6965, 2.431, 1.8219, 0.2241, 0.9694, 0.9954]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0074,Model_Drift,"[2.3302, -0.3651, 2.6532, 0.3636, 1.982, -3.0942, 0.9984, 2.0265, 3.4609, 4.0958]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0075,Model_Drift,"[0.3, 2.0284, -2.5415, 1.6528, 1.8802, 3.7444, 0.5524, 2.5431, 0.4836, -2.848]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0076,Model_Drift,"[3.0494, -3.9825, 4.0946, -1.1267, -3.3317, 0.0844, 0.0809, -1.2436, 4.6759, 4.629]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0077,Model_Drift,"[5.1074, 4.713, 0.9691, -0.4189, 8.6385, -0.4851, 2.1059, 3.5474, 2.7928, 3.9611]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0078,Model_Drift,"[3.9056, 2.2359, -0.2215, -2.9474, 7.0361, 1.6014, 1.354, 6.0374, 1.088, 4.7476]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0079,Model_Drift,"[3.1956, -0.1065, 2.2464, 0.4861, 3.7761, 1.6169, 0.5755, 4.372, -1.3799, 6.7588]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0080,Model_Drift,"[1.9873, -0.5178, -0.4596, -2.4222, 2.8278, 0.6562, 0.8641, 5.1073, 2.6559, 2.8589]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0081,Model_Drift,"[-0.0501, 5.2968, -1.8674, 5.6746, 0.8867, 3.4206, 2.3991, -2.3594, 2.1158, -7.1167]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0082,Model_Drift,"[0.4794, 0.6211, 2.3593, 3.1966, 0.0174, 0.845, 3.0991, 2.3002, 2.2661, 2.2238]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0083,Model_Drift,"[-0.9048, 3.4303, -0.7256, 3.5522, 0.3187, 3.7602, 5.771, 1.951, 1.3282, -2.5134]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0084,Model_Drift,"[3.0033, 1.4868, 1.3657, 0.1801, 1.5948, 1.6679, 3.6379, -0.2894, 1.235, 2.1714]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0085,Model_Drift,"[-0.5397, 1.3707, -2.8413, 4.9766, -0.5672, 2.1574, 3.3947, -0.1541, -0.2923, -4.5692]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0086,Model_Drift,"[1.7318, 0.4218, -0.1476, 2.9925, -0.2345, 5.432, 4.5299, 2.0097, 1.3237, 0.7652]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0087,Model_Drift,"[2.5377, 3.1542, 3.8408, 0.8365, 3.3983, 1.2897, 3.7156, 1.8879, 0.7646, 5.0079]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0088,Model_Drift,"[1.2042, 1.9033, 2.3534, 1.2364, 0.5587, 4.2166, 1.8734, 1.2883, 0.9164, 1.92]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0089,Model_Drift,"[0.135, 2.989, 0.0452, 1.5474, 2.4534, 1.0305, 0.8301, 2.3216, 1.8259, -1.0713]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0090,Model_Drift,"[-0.1424, -0.796, 1.138, 6.2571, 1.2698, -1.3654, -1.2174, 5.7488, 4.3639, 1.398]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0091,Model_Drift,"[1.1128, 1.8936, 1.8283, 0.6937, 0.3211, 4.3319, 2.5786, 1.2198, 1.3257, 1.3354]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0092,Model_Drift,"[1.1096, 0.4545, 3.3665, 0.4906, -0.5085, 3.8328, 3.2332, 1.86, 0.3135, 4.2167]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0093,Model_Drift,"[-2.4385, 1.2548, 2.3852, 0.6412, -1.2498, 0.0131, 2.1481, 2.4507, 1.4291, 0.4784]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0094,Model_Drift,"[3.4329, 1.1726, 1.7352, 2.9938, 4.4008, -0.3365, 1.61, 3.2744, 1.1835, 4.5288]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0095,Model_Drift,"[0.951, -0.4342, -0.2384, -2.5725, -0.526, 3.2186, 2.8739, 2.0149, 1.5032, 0.9917]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0096,Model_Drift,"[2.3479, -0.1709, 0.4915, 2.0605, 0.0245, 3.606, 0.8037, 0.0272, 0.2805, 1.0032]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0097,Model_Drift,"[-2.3953, -3.6697, 1.6108, 0.568, -5.8518, 6.6633, -0.3626, 2.9904, -1.3127, 1.5938]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0098,Model_Drift,"[3.9145, -1.4712, 4.2401, 0.9297, 0.61, -2.3959, -0.0439, -0.4374, 4.3446, 5.1461]",0,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
DRF_0099,Model_Drift,"[-1.3703, 2.0997, 0.1439, 2.0872, 1.1175, 0.0143, -0.8816, 2.8232, 2.669, -2.0749]",1,0.8867,0.84,0.8923,0.86,0.8586,Model accuracy degrades as real-world data distribution shifts over time.,Input feature distribution changed after deployment (concept drift).,"Monitor production metrics, set drift detectors, retrain periodically, use online learning.",High,"{'period_0': 0.8867, 'period_9': 0.84}"
OOD_0000,Out_of_Distribution,"[3.1873, 1.8424]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0001,Out_of_Distribution,"[4.0706, 2.5888]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0002,Out_of_Distribution,"[3.9735, 1.7082]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0003,Out_of_Distribution,"[2.9758, 0.7896]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0004,Out_of_Distribution,"[3.4104, 2.6623]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0005,Out_of_Distribution,"[2.5613, 0.751]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0006,Out_of_Distribution,"[4.1375, 1.7113]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0007,Out_of_Distribution,"[3.2131, 1.0559]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0008,Out_of_Distribution,"[4.2767, 2.0707]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0009,Out_of_Distribution,"[5.5792, 0.7292]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0010,Out_of_Distribution,"[4.1904, 1.096]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0011,Out_of_Distribution,"[1.9554, 2.0501]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0012,Out_of_Distribution,"[2.6238, 0.402]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0013,Out_of_Distribution,"[3.2369, 2.3752]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0014,Out_of_Distribution,"[3.645, 2.7131]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0015,Out_of_Distribution,"[3.1733, 1.8852]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0016,Out_of_Distribution,"[3.2299, 1.1177]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0017,Out_of_Distribution,"[3.2209, -0.0521]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0018,Out_of_Distribution,"[3.5296, 2.2363]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0019,Out_of_Distribution,"[3.0617, 1.3956]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0020,Out_of_Distribution,"[4.3437, -0.2949]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0021,Out_of_Distribution,"[3.7026, 2.2564]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0022,Out_of_Distribution,"[3.8899, 1.8998]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0023,Out_of_Distribution,"[5.1051, 2.9038]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0024,Out_of_Distribution,"[3.8613, 2.3566]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0025,Out_of_Distribution,"[2.8933, 1.4919]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0026,Out_of_Distribution,"[4.6869, 1.4635]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0027,Out_of_Distribution,"[2.9841, 2.7087]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0028,Out_of_Distribution,"[2.6527, 1.49]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0029,Out_of_Distribution,"[4.6939, 0.9185]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0030,Out_of_Distribution,"[3.5041, 1.774]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0031,Out_of_Distribution,"[2.6753, 3.2127]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0032,Out_of_Distribution,"[3.7307, 2.9656]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0033,Out_of_Distribution,"[3.1305, 1.0192]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0034,Out_of_Distribution,"[3.3739, 2.2621]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0035,Out_of_Distribution,"[2.6348, 2.3019]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0036,Out_of_Distribution,"[3.8464, 2.5854]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0037,Out_of_Distribution,"[2.5345, 1.936]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0038,Out_of_Distribution,"[3.5281, 1.7684]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0039,Out_of_Distribution,"[3.6616, 2.6689]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0040,Out_of_Distribution,"[4.1257, 2.7212]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0041,Out_of_Distribution,"[4.7379, 0.9749]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0042,Out_of_Distribution,"[2.4337, 2.576]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0043,Out_of_Distribution,"[4.099, -0.0423]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0044,Out_of_Distribution,"[3.2507, 1.8671]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0045,Out_of_Distribution,"[5.3248, 2.2325]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0046,Out_of_Distribution,"[4.1265, 0.9294]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0047,Out_of_Distribution,"[3.6872, 1.0984]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0048,Out_of_Distribution,"[3.9699, -0.1617]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0049,Out_of_Distribution,"[2.4003, 1.3887]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0050,Out_of_Distribution,"[3.5125, 1.4487]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0051,Out_of_Distribution,"[2.931, 0.5024]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0052,Out_of_Distribution,"[3.0952, 1.5856]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0053,Out_of_Distribution,"[3.4805, 1.781]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0054,Out_of_Distribution,"[3.1358, 1.1166]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0055,Out_of_Distribution,"[4.5675, -0.7279]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0056,Out_of_Distribution,"[3.9368, 1.6519]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0057,Out_of_Distribution,"[1.5684, 1.4982]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0058,Out_of_Distribution,"[3.2041, 2.4258]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0059,Out_of_Distribution,"[3.9601, 1.8457]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0060,Out_of_Distribution,"[2.7195, 2.3216]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0061,Out_of_Distribution,"[2.7093, 0.4316]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0062,Out_of_Distribution,"[4.9776, 0.2151]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0063,Out_of_Distribution,"[2.6205, 1.7447]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0064,Out_of_Distribution,"[3.1773, 2.4641]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0065,Out_of_Distribution,"[3.0104, 1.5185]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0066,Out_of_Distribution,"[3.1404, 2.8557]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0067,Out_of_Distribution,"[3.9689, 2.0907]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0068,Out_of_Distribution,"[4.0754, 0.1071]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0069,Out_of_Distribution,"[2.9068, 2.3863]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0070,Out_of_Distribution,"[2.8103, -0.0602]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0071,Out_of_Distribution,"[3.3193, 0.5775]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0072,Out_of_Distribution,"[2.5686, 1.0626]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0073,Out_of_Distribution,"[4.1102, 0.5184]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0074,Out_of_Distribution,"[4.4489, 3.2547]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0075,Out_of_Distribution,"[3.0527, 1.4249]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0076,Out_of_Distribution,"[3.5829, 1.0154]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0077,Out_of_Distribution,"[3.1585, 1.239]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0078,Out_of_Distribution,"[2.6399, 1.2794]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0079,Out_of_Distribution,"[4.7348, 2.5919]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0080,Out_of_Distribution,"[3.8883, 2.6451]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0081,Out_of_Distribution,"[4.0958, 0.5547]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0082,Out_of_Distribution,"[4.253, 1.0614]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0083,Out_of_Distribution,"[3.2537, 1.7144]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0084,Out_of_Distribution,"[3.1311, 2.2845]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0085,Out_of_Distribution,"[3.531, 0.7166]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0086,Out_of_Distribution,"[4.079, 1.5431]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0087,Out_of_Distribution,"[4.4414, 1.9258]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0088,Out_of_Distribution,"[3.6257, 1.0535]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0089,Out_of_Distribution,"[5.6476, 2.3107]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0090,Out_of_Distribution,"[3.2286, 0.9114]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0091,Out_of_Distribution,"[1.9345, 1.6982]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0092,Out_of_Distribution,"[2.7054, 0.2194]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0093,Out_of_Distribution,"[4.5805, 0.7793]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0094,Out_of_Distribution,"[3.905, 0.6778]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0095,Out_of_Distribution,"[3.2058, 2.4497]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0096,Out_of_Distribution,"[2.9353, 2.0468]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0097,Out_of_Distribution,"[4.686, 1.5707]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0098,Out_of_Distribution,"[3.3091, -0.3589]",1,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"
OOD_0099,Out_of_Distribution,"[3.5507, 3.1234]",0,0.975,0.515,0.611,0.515,0.3812,Model fails on data far outside training distribution.,No exposure to OOD samples; model extrapolates incorrectly.,"OOD detection, confidence thresholding, diverse data augmentation, MC Dropout uncertainty.",Critical,"{'in_dist_acc': 0.975, 'ood_acc': 0.515}"