Datasets:
mdsajjadullah
/
ml-failures-dataset

Dataset card Data Studio Files
xet
Community
1
scenario_id
string
failure_type
string
feature_values
string
true_label
int64
train_accuracy
float64
val_accuracy
float64
precision
float64
recall
float64
f1_score
float64
error_description
string
root_cause
string
fix_strategy
string
severity
string
extra_info
string
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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
0.675
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}