| { | |
| "name": "2dplanes", | |
| "n_num_features": 10, | |
| "n_cat_features": 0, | |
| "train_size": 26091, | |
| "val_size": 6523, | |
| "test_size": 8154, | |
| "source": "https://www.openml.org/search?type=data&status=active&id=215&sort=runs", | |
| "task_intro": "**Author**: \n**Source**: Unknown - \n**Please cite**: \n\nThis is an artificial data set described in Breiman et al. (1984,p.238) \n (with variance 1 instead of 2). \n \n Generate the values of the 10 attributes independently\n using the following probabilities:\n\n P(X_1 = -1) = P(X_1 = 1) = 1/2\n P(X_m = -1) = P(X_m = 0) = P(X_m = 1) = 1/3, m=2,...,10\n\n Obtain the value of the target variable Y using the rule:\n\n if X_1 = 1 set Y = 3 + 3X_2 + 2X_3 + X_4 + sigma(0,1)\n if X_1 = -1 set Y = -3 + 3X_5 + 2X_6 + X_7 + sigma(0,1)\n\n Characteristics: 40768 cases, 11 continuous attributes\n Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at\n http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html\n Original source: Breiman et al. (1984, p.238).", | |
| "task_type": "regression", | |
| "openml_id": 215, | |
| "n_classes": 1, | |
| "num_feature_intro": { | |
| "x1": "x1", | |
| "x2": "x2", | |
| "x3": "x3", | |
| "x4": "x4", | |
| "x5": "x5", | |
| "x6": "x6", | |
| "x7": "x7", | |
| "x8": "x8", | |
| "x9": "x9", | |
| "x10": "x10" | |
| }, | |
| "cat_feature_intro": {} | |
| } |