10591747 31244 Sharath Kumar Reddy Alijarla 18 Supervised Classification 18983 sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(2) 8304520 Python_3.7.3. Sklearn_0.20.0. NumPy_1.21.5. SciPy_1.7.3. n_jobs null 18952 remainder "passthrough" 18952 sparse_threshold 0.3 18952 transformer_weights null 18952 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}] 18952 memory null 18953 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] 18953 axis 0 18954 copy true 18954 missing_values "NaN" 18954 strategy "mean" 18954 verbose 0 18954 copy true 18955 with_mean true 18955 with_std true 18955 memory null 18956 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}] 18956 copy true 18957 fill_value -1 18957 missing_values NaN 18957 strategy "constant" 18957 verbose 0 18957 categorical_features null 18958 categories null 18958 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 18958 handle_unknown "ignore" 18958 n_values null 18958 sparse true 18958 threshold 0.0 18959 class_weight null 18971 criterion "entropy" 18971 max_depth null 18971 max_features 1.0 18971 max_leaf_nodes null 18971 min_impurity_decrease 0.0 18971 min_impurity_split null 18971 min_samples_leaf 1 18971 min_samples_split 2 18971 min_weight_fraction_leaf 0.0 18971 presort false 18971 random_state 0 18971 splitter "best" 18971 memory null 18983 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}] 18983 openml-python Sklearn_0.20.0. 18 mfeat-morphological https://www.openml.org/data/download/18/dataset_18_mfeat-morphological.arff -1 22111627 description https://api.openml.org/data/download/22111627/description.xml -1 22111628 predictions https://api.openml.org/data/download/22111628/predictions.arff area_under_roc_curve 0.8142334722222223 [0.991944,0.927222,0.776389,0.714444,0.791111,0.793056,0.635437,0.8975,0.992222,0.623008] average_cost 0 f_measure 0.6516286563048691 [0.987469,0.865672,0.592593,0.494845,0.632653,0.622222,0.329466,0.801956,0.98995,0.199461] kappa 0.6144444444444445 kb_relative_information_score 0.6456674607247957 mean_absolute_error 0.06901666666666692 mean_prior_absolute_error 0.18000000000000554 weighted_recall 0.653 [0.985,0.87,0.6,0.48,0.62,0.63,0.355,0.82,0.985,0.185] number_of_instances 2000 [200,200,200,200,200,200,200,200,200,200] precision 0.6511179585093215 [0.98995,0.861386,0.585366,0.510638,0.645833,0.614634,0.307359,0.784689,0.994949,0.216374] predictive_accuracy 0.653 prior_entropy 3.3219280948872383 relative_absolute_error 0.3834259259259155 root_mean_prior_squared_error 0.3000000000000046 root_mean_squared_error 0.25976485092825435 root_relative_squared_error 0.8658828364275013 total_cost 0 unweighted_recall 0.6530000000000001 [0.985,0.87,0.6,0.48,0.62,0.63,0.355,0.82,0.985,0.185] area_under_roc_curve 0.7920277777777778 [0.975,0.869444,0.780556,0.608333,0.747222,0.794444,0.61375,0.886111,1,0.645417] area_under_roc_curve 0.8087777777777779 [0.997222,0.913889,0.672222,0.705556,0.825,0.805556,0.674444,0.886111,1,0.607778] area_under_roc_curve 0.8196805555555556 [0.972222,0.975,0.836111,0.802778,0.758333,0.683333,0.61,0.888889,1,0.670139] area_under_roc_curve 0.8089999999999999 [1,0.955556,0.794444,0.719444,0.736111,0.769444,0.603611,0.888889,1,0.6225] area_under_roc_curve 0.8206111111111111 [1,0.975,0.827778,0.647222,0.827778,0.786111,0.650278,0.908333,1,0.583611] area_under_roc_curve 0.8383888888888891 [1,0.941667,0.711111,0.844444,0.838889,0.8,0.655833,0.891667,1,0.700278] area_under_roc_curve 0.8220555555555557 [0.975,0.888889,0.838889,0.713889,0.830556,0.836111,0.673333,0.911111,0.972222,0.580556] area_under_roc_curve 0.830875 [1,0.941667,0.769444,0.736111,0.844444,0.852778,0.623611,0.961111,0.975,0.604583] area_under_roc_curve 0.7986666666666666 [1,0.866667,0.769444,0.772222,0.772222,0.738889,0.579722,0.891667,1,0.595833] area_under_roc_curve 0.8029444444444445 [1,0.944444,0.763889,0.594444,0.730556,0.863889,0.674444,0.861111,0.975,0.621667] average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 f_measure 0.6115392298319127 [0.974359,0.810811,0.615385,0.292683,0.536585,0.590909,0.27027,0.780488,1,0.243902] f_measure 0.6375356211996539 [0.97561,0.829268,0.421053,0.5,0.651163,0.65,0.391304,0.780488,1,0.176471] f_measure 0.6685055850687829 [0.95,0.974359,0.612245,0.634146,0.594595,0.470588,0.315789,0.8,1,0.333333] f_measure 0.6386306798107955 [1,0.826087,0.705882,0.487805,0.571429,0.55814,0.27907,0.8,1,0.157895] f_measure 0.6652401136122065 [1,0.974359,0.666667,0.378378,0.666667,0.648649,0.355556,0.790698,1,0.171429] f_measure 0.6932778857697355 [1,0.878049,0.529412,0.652174,0.736842,0.619048,0.380952,0.820513,1,0.315789] f_measure 0.6552707728990853 [0.974359,0.8,0.625,0.545455,0.682927,0.717949,0.382979,0.809524,0.95,0.064516] f_measure 0.6810674810788252 [1,0.878049,0.55814,0.571429,0.777778,0.697674,0.318182,0.863636,0.974359,0.171429] f_measure 0.633824286682576 [1,0.789474,0.55814,0.571429,0.571429,0.588235,0.243902,0.820513,1,0.195122] f_measure 0.6168754390182962 [1,0.9,0.628571,0.277778,0.540541,0.653061,0.333333,0.75,0.974359,0.111111] kappa 0.5666666666666667 kappa 0.6055555555555556 kappa 0.6333333333333334 kappa 0.6 kappa 0.6333333333333334 kappa 0.6611111111111111 kappa 0.6277777777777779 kappa 0.65 kappa 0.5888888888888889 kappa 0.5777777777777778 kb_relative_information_score 0.6031583723273549 kb_relative_information_score 0.6354115533394539 kb_relative_information_score 0.6548215531723472 kb_relative_information_score 0.6360362470224952 kb_relative_information_score 0.6593370031073071 kb_relative_information_score 0.6884127653853314 kb_relative_information_score 0.6591698843298723 kb_relative_information_score 0.6772418529618808 kb_relative_information_score 0.6190118534631999 kb_relative_information_score 0.6240735221385685 mean_absolute_error 0.07716666666666655 mean_absolute_error 0.07099999999999991 mean_absolute_error 0.06749999999999992 mean_absolute_error 0.07066666666666657 mean_absolute_error 0.06599999999999992 mean_absolute_error 0.06149999999999993 mean_absolute_error 0.0666666666666666 mean_absolute_error 0.06299999999999993 mean_absolute_error 0.0734999999999999 mean_absolute_error 0.07316666666666656 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] precision 0.615924001179419 [1,0.882353,0.631579,0.285714,0.52381,0.541667,0.294118,0.761905,1,0.238095] precision 0.6349889180867442 [0.952381,0.809524,0.444444,0.5625,0.608696,0.65,0.346154,0.761905,1,0.214286] precision 0.67562915448311 [0.95,1,0.517241,0.619048,0.647059,0.571429,0.333333,0.8,1,0.318182] precision 0.6480044593088071 [1,0.730769,0.857143,0.47619,0.666667,0.521739,0.26087,0.8,1,0.166667] precision 0.6649504766333411 [1,1,0.636364,0.411765,0.636364,0.705882,0.32,0.73913,1,0.2] precision 0.6984684905737537 [1,0.857143,0.642857,0.576923,0.777778,0.590909,0.363636,0.842105,1,0.333333] precision 0.6578500446921499 [1,0.8,0.535714,0.692308,0.666667,0.736842,0.333333,0.772727,0.95,0.090909] precision 0.6856055900621117 [1,0.857143,0.521739,0.666667,0.875,0.652174,0.291667,0.791667,1,0.2] precision 0.6430943960692244 [1,0.833333,0.521739,0.545455,0.545455,0.714286,0.238095,0.842105,1,0.190476] precision 0.6246507051096301 [1,0.9,0.733333,0.3125,0.588235,0.551724,0.285714,0.75,1,0.125] predictive_accuracy 0.61 predictive_accuracy 0.645 predictive_accuracy 0.67 predictive_accuracy 0.64 predictive_accuracy 0.67 predictive_accuracy 0.695 predictive_accuracy 0.665 predictive_accuracy 0.685 predictive_accuracy 0.63 predictive_accuracy 0.62 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 relative_absolute_error 0.42870370370370353 relative_absolute_error 0.3944444444444444 relative_absolute_error 0.375 relative_absolute_error 0.39259259259259255 relative_absolute_error 0.36666666666666664 relative_absolute_error 0.3416666666666666 relative_absolute_error 0.3703703703703704 relative_absolute_error 0.35 relative_absolute_error 0.4083333333333332 relative_absolute_error 0.40648148148148133 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_squared_error 0.2751262336536528 root_mean_squared_error 0.2636285265292812 root_mean_squared_error 0.25641762809916163 root_mean_squared_error 0.2635231383473648 root_mean_squared_error 0.25495097567963915 root_mean_squared_error 0.24341322889276165 root_mean_squared_error 0.2543510260337953 root_mean_squared_error 0.24799193535274483 root_mean_squared_error 0.2697220791852234 root_mean_squared_error 0.26682287091710183 root_relative_squared_error 0.9170874455121765 root_relative_squared_error 0.8787617550976045 root_relative_squared_error 0.8547254269972059 root_relative_squared_error 0.8784104611578831 root_relative_squared_error 0.8498365855987976 root_relative_squared_error 0.8113774296425393 root_relative_squared_error 0.8478367534459847 root_relative_squared_error 0.82663978450915 root_relative_squared_error 0.8990735972840785 root_relative_squared_error 0.8894095697236734 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 unweighted_recall 0.6099999999999999 [0.95,0.75,0.6,0.3,0.55,0.65,0.25,0.8,1,0.25] unweighted_recall 0.6450000000000001 [1,0.85,0.4,0.45,0.7,0.65,0.45,0.8,1,0.15] unweighted_recall 0.6699999999999999 [0.95,0.95,0.75,0.65,0.55,0.4,0.3,0.8,1,0.35] unweighted_recall 0.6399999999999999 [1,0.95,0.6,0.5,0.5,0.6,0.3,0.8,1,0.15] unweighted_recall 0.67 [1,0.95,0.7,0.35,0.7,0.6,0.4,0.85,1,0.15] unweighted_recall 0.6950000000000001 [1,0.9,0.45,0.75,0.7,0.65,0.4,0.8,1,0.3] unweighted_recall 0.665 [0.95,0.8,0.75,0.45,0.7,0.7,0.45,0.85,0.95,0.05] unweighted_recall 0.685 [1,0.9,0.6,0.5,0.7,0.75,0.35,0.95,0.95,0.15] unweighted_recall 0.6300000000000001 [1,0.75,0.6,0.6,0.6,0.5,0.25,0.8,1,0.2] unweighted_recall 0.62 [1,0.9,0.55,0.25,0.5,0.8,0.4,0.75,0.95,0.1] usercpu_time_millis 15.625 usercpu_time_millis 15.625 usercpu_time_millis 15.625 usercpu_time_millis 15.625 usercpu_time_millis 15.625 usercpu_time_millis 15.625 usercpu_time_millis 15.625 usercpu_time_millis 15.625 usercpu_time_millis 15.625 usercpu_time_millis 31.25 usercpu_time_millis_testing 0 usercpu_time_millis_testing 0 usercpu_time_millis_testing 0 usercpu_time_millis_testing 0 usercpu_time_millis_testing 15.625 usercpu_time_millis_testing 0 usercpu_time_millis_testing 0 usercpu_time_millis_testing 0 usercpu_time_millis_testing 0 usercpu_time_millis_testing 15.625 usercpu_time_millis_training 15.625 usercpu_time_millis_training 15.625 usercpu_time_millis_training 15.625 usercpu_time_millis_training 15.625 usercpu_time_millis_training 0 usercpu_time_millis_training 15.625 usercpu_time_millis_training 15.625 usercpu_time_millis_training 15.625 usercpu_time_millis_training 15.625 usercpu_time_millis_training 15.625 wall_clock_time_millis 16.04175567626953 wall_clock_time_millis 17.046451568603516 wall_clock_time_millis 16.019344329833984 wall_clock_time_millis 16.07060432434082 wall_clock_time_millis 16.04175567626953 wall_clock_time_millis 17.076969146728516 wall_clock_time_millis 16.041278839111328 wall_clock_time_millis 17.072439193725586 wall_clock_time_millis 16.048192977905273 wall_clock_time_millis 17.046451568603516 wall_clock_time_millis_testing 1.003265380859375 wall_clock_time_millis_testing 2.005338668823242 wall_clock_time_millis_testing 1.0030269622802734 wall_clock_time_millis_testing 1.0008811950683594 wall_clock_time_millis_testing 1.003265380859375 wall_clock_time_millis_testing 1.0027885437011719 wall_clock_time_millis_testing 1.0020732879638672 wall_clock_time_millis_testing 1.0013580322265625 wall_clock_time_millis_testing 1.0097026824951172 wall_clock_time_millis_testing 1.0044574737548828 wall_clock_time_millis_training 15.038490295410156 wall_clock_time_millis_training 15.041112899780273 wall_clock_time_millis_training 15.016317367553711 wall_clock_time_millis_training 15.069723129272461 wall_clock_time_millis_training 15.038490295410156 wall_clock_time_millis_training 16.074180603027344 wall_clock_time_millis_training 15.039205551147461 wall_clock_time_millis_training 16.071081161499023 wall_clock_time_millis_training 15.038490295410156 wall_clock_time_millis_training 16.041994094848633 weighted_recall 0.61 [0.95,0.75,0.6,0.3,0.55,0.65,0.25,0.8,1,0.25] weighted_recall 0.645 [1,0.85,0.4,0.45,0.7,0.65,0.45,0.8,1,0.15] weighted_recall 0.67 [0.95,0.95,0.75,0.65,0.55,0.4,0.3,0.8,1,0.35] weighted_recall 0.64 [1,0.95,0.6,0.5,0.5,0.6,0.3,0.8,1,0.15] weighted_recall 0.67 [1,0.95,0.7,0.35,0.7,0.6,0.4,0.85,1,0.15] weighted_recall 0.695 [1,0.9,0.45,0.75,0.7,0.65,0.4,0.8,1,0.3] weighted_recall 0.665 [0.95,0.8,0.75,0.45,0.7,0.7,0.45,0.85,0.95,0.05] weighted_recall 0.685 [1,0.9,0.6,0.5,0.7,0.75,0.35,0.95,0.95,0.15] weighted_recall 0.63 [1,0.75,0.6,0.6,0.6,0.5,0.25,0.8,1,0.2] weighted_recall 0.62 [1,0.9,0.55,0.25,0.5,0.8,0.4,0.75,0.95,0.1]