10576387 28997 Marc Boel 16 Supervised Classification 19037 sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier)(1) 8292669 Python_3.8.10. Sklearn_0.24.2. NumPy_1.17.4. SciPy_1.3.3. add_indicator false 18819 copy true 18819 fill_value null 18819 missing_values NaN 18819 strategy "mean" 18819 verbose 0 18819 n_jobs null 18996 remainder "drop" 18996 sparse_threshold 0.3 18996 transformer_weights null 18996 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}] 18996 verbose false 18996 categories "auto" 18997 drop null 18997 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 18997 handle_unknown "ignore" 18997 sparse true 18997 memory null 19037 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}] 19037 verbose false 19037 ccp_alpha 0.0 19038 criterion "friedman_mse" 19038 init null 19038 learning_rate 0.7726525725225382 19038 loss "deviance" 19038 max_depth 3 19038 max_features null 19038 max_leaf_nodes 1782 19038 min_impurity_decrease 0.0 19038 min_impurity_split null 19038 min_samples_leaf 7 19038 min_samples_split 2 19038 min_weight_fraction_leaf 0.0 19038 n_estimators 100 19038 n_iter_no_change 4 19038 random_state 41570 19038 subsample 1.0 19038 tol 0.0001 19038 validation_fraction 0.20881732449420107 19038 verbose 0 19038 warm_start false 19038 openml-python Sklearn_0.24.2. 16 mfeat-karhunen https://www.openml.org/data/download/16/dataset_16_mfeat-karhunen.arff -1 22079041 description https://api.openml.org/data/download/22079041/description.xml -1 22079042 predictions https://api.openml.org/data/download/22079042/predictions.arff area_under_roc_curve 0.9626894444444445 [0.979,0.963657,0.972353,0.951657,0.972281,0.947619,0.9425,0.990139,0.932994,0.974694] average_cost 0 f_measure 0.8117323554275226 [0.836186,0.808717,0.875318,0.84131,0.838875,0.765306,0.763819,0.865823,0.720988,0.800983] kappa 0.7905555555555556 kb_relative_information_score 0.796477800355245 mean_absolute_error 0.044967405383892815 mean_prior_absolute_error 0.18000000000000554 weighted_recall 0.8115 [0.855,0.835,0.86,0.835,0.82,0.75,0.76,0.855,0.73,0.815] number_of_instances 2000 [200,200,200,200,200,200,200,200,200,200] precision 0.8125250146285565 [0.818182,0.784038,0.891192,0.847716,0.858639,0.78125,0.767677,0.876923,0.712195,0.78744] predictive_accuracy 0.8115000000000001 prior_entropy 3.3219280948872383 relative_absolute_error 0.24981891879939686 root_mean_prior_squared_error 0.3000000000000046 root_mean_squared_error 0.1697214605555696 root_relative_squared_error 0.56573820185189 total_cost 0 unweighted_recall 0.8115 [0.855,0.835,0.86,0.835,0.82,0.75,0.76,0.855,0.73,0.815] area_under_roc_curve 0.969527777777778 [0.963889,0.921944,0.997778,0.965833,0.998333,0.971111,0.925278,0.996944,0.964167,0.99] area_under_roc_curve 0.9696388888888889 [0.962778,0.989722,0.981389,0.955278,0.958056,0.915278,0.975,0.9925,0.973056,0.993333] area_under_roc_curve 0.9608611111111113 [0.981111,0.977778,0.956667,0.856944,0.946389,0.976111,0.983056,0.990833,0.941667,0.998056] area_under_roc_curve 0.977 [0.987778,0.975278,0.997222,0.981389,0.970278,0.961389,0.983889,0.994444,0.946389,0.971944] area_under_roc_curve 0.9430833333333333 [0.977222,0.939444,0.947778,0.925139,0.967778,0.948889,0.94,0.977222,0.867778,0.939583] area_under_roc_curve 0.9571388888888889 [0.993611,0.948889,0.99,0.995,0.982778,0.952222,0.823333,0.996389,0.955,0.934167] area_under_roc_curve 0.9549444444444444 [0.960833,0.971111,0.874583,0.965833,0.931667,0.993611,0.973333,0.9775,0.92375,0.977222] area_under_roc_curve 0.9651944444444445 [0.990833,0.964167,0.997222,0.995833,0.968889,0.978611,0.888333,0.995,0.888056,0.985] area_under_roc_curve 0.9569583333333334 [0.988889,0.956944,0.999167,0.885833,0.998611,0.866389,0.964722,0.999167,0.917361,0.9925] area_under_roc_curve 0.9703611111111111 [0.975278,0.998056,0.964167,0.980833,0.983333,0.933333,0.961944,0.976667,0.962778,0.967222] 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 0.8408452214487623 [0.790698,0.8,0.904762,0.857143,0.926829,0.8,0.833333,0.864865,0.864865,0.765957] f_measure 0.8325289378172779 [0.878049,0.871795,0.923077,0.780488,0.842105,0.647059,0.837209,0.871795,0.829268,0.844444] f_measure 0.8069568909378539 [0.772727,0.9,0.789474,0.777778,0.789474,0.723404,0.772727,0.857143,0.736842,0.95] f_measure 0.825664765615806 [0.9,0.871795,0.894737,0.923077,0.809524,0.702703,0.790698,0.9,0.727273,0.736842] f_measure 0.7575459659737356 [0.777778,0.634146,0.842105,0.8,0.842105,0.780488,0.75,0.8,0.55814,0.790698] f_measure 0.7881571131571131 [0.857143,0.75,0.923077,0.857143,0.810811,0.75,0.540541,0.9,0.75,0.742857] f_measure 0.7817714553553519 [0.857143,0.789474,0.742857,0.820513,0.769231,0.837209,0.761905,0.837209,0.652174,0.75] f_measure 0.816629787117592 [0.844444,0.857143,0.926829,0.85,0.8,0.769231,0.829268,0.85,0.628571,0.810811] f_measure 0.8267681695780168 [0.809524,0.772727,0.95,0.820513,0.95,0.709677,0.75,0.904762,0.736842,0.863636] f_measure 0.8334327122405095 [0.878049,0.851064,0.837209,0.923077,0.842105,0.9,0.75,0.871795,0.744186,0.736842] kappa 0.8222222222222222 kappa 0.8166666666666667 kappa 0.7833333333333334 kappa 0.8055555555555555 kappa 0.7277777777777777 kappa 0.7666666666666667 kappa 0.7555555555555555 kappa 0.7999999999999999 kappa 0.8111111111111111 kappa 0.8166666666666667 kb_relative_information_score 0.8273829083704116 kb_relative_information_score 0.8095892479770653 kb_relative_information_score 0.7855285232420407 kb_relative_information_score 0.8080064761504606 kb_relative_information_score 0.7458621693891756 kb_relative_information_score 0.7715607113699003 kb_relative_information_score 0.7626457935982053 kb_relative_information_score 0.8148349575381296 kb_relative_information_score 0.8274695313603603 kb_relative_information_score 0.8118976845564393 mean_absolute_error 0.03982149836197102 mean_absolute_error 0.04206807492360519 mean_absolute_error 0.04628104454585002 mean_absolute_error 0.043505903812816424 mean_absolute_error 0.05489286847573453 mean_absolute_error 0.04905837167554886 mean_absolute_error 0.05129952387249884 mean_absolute_error 0.041357273880137414 mean_absolute_error 0.03819255950906757 mean_absolute_error 0.04319693478169753 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 0.8545563462539165 [0.73913,0.933333,0.863636,0.818182,0.904762,0.8,0.9375,0.941176,0.941176,0.666667] precision 0.8382625404089933 [0.857143,0.894737,0.947368,0.761905,0.888889,0.785714,0.782609,0.894737,0.809524,0.76] precision 0.8215740740740742 [0.708333,0.9,0.833333,0.875,0.833333,0.62963,0.708333,1,0.777778,0.95] precision 0.8307557741909606 [0.9,0.894737,0.944444,0.947368,0.772727,0.764706,0.73913,0.9,0.666667,0.777778] precision 0.764459972394755 [0.875,0.619048,0.888889,0.8,0.888889,0.761905,0.75,0.8,0.521739,0.73913] precision 0.7963844102234195 [0.818182,0.642857,0.947368,0.818182,0.882353,0.75,0.588235,0.9,0.75,0.866667] precision 0.7950992142868575 [1,0.833333,0.866667,0.842105,0.789474,0.782609,0.727273,0.782609,0.576923,0.75] precision 0.8197627491187862 [0.76,0.818182,0.904762,0.85,0.8,0.789474,0.809524,0.85,0.733333,0.882353] precision 0.8406246677299309 [0.772727,0.708333,0.95,0.842105,0.95,1,0.75,0.863636,0.777778,0.791667] precision 0.8484916397273377 [0.857143,0.740741,0.782609,0.947368,0.888889,0.9,1,0.894737,0.695652,0.777778] predictive_accuracy 0.84 predictive_accuracy 0.835 predictive_accuracy 0.805 predictive_accuracy 0.825 predictive_accuracy 0.755 predictive_accuracy 0.79 predictive_accuracy 0.78 predictive_accuracy 0.82 predictive_accuracy 0.83 predictive_accuracy 0.835 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 0.2212305464553948 relative_absolute_error 0.23371152735336242 relative_absolute_error 0.25711691414361154 relative_absolute_error 0.24169946562675818 relative_absolute_error 0.30496038042074775 relative_absolute_error 0.2725465093086051 relative_absolute_error 0.28499735484721606 relative_absolute_error 0.22976263266743033 relative_absolute_error 0.2121808861614867 relative_absolute_error 0.23998297100943103 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 0.15902596028441215 root_mean_squared_error 0.16370473371877553 root_mean_squared_error 0.17121894249060868 root_mean_squared_error 0.16499943292966396 root_mean_squared_error 0.18673436943101465 root_mean_squared_error 0.17841122779445492 root_mean_squared_error 0.18731483196984236 root_mean_squared_error 0.16402590976537376 root_mean_squared_error 0.1579434400430764 root_mean_squared_error 0.16058250271221106 root_relative_squared_error 0.5300865342813741 root_relative_squared_error 0.5456824457292521 root_relative_squared_error 0.5707298083020294 root_relative_squared_error 0.5499981097655469 root_relative_squared_error 0.6224478981033825 root_relative_squared_error 0.5947040926481835 root_relative_squared_error 0.6243827732328082 root_relative_squared_error 0.5467530325512462 root_relative_squared_error 0.5264781334769216 root_relative_squared_error 0.5352750090407038 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 0.8399999999999999 [0.85,0.7,0.95,0.9,0.95,0.8,0.75,0.8,0.8,0.9] unweighted_recall 0.835 [0.9,0.85,0.9,0.8,0.8,0.55,0.9,0.85,0.85,0.95] unweighted_recall 0.8049999999999999 [0.85,0.9,0.75,0.7,0.75,0.85,0.85,0.75,0.7,0.95] unweighted_recall 0.825 [0.9,0.85,0.85,0.9,0.85,0.65,0.85,0.9,0.8,0.7] unweighted_recall 0.7549999999999999 [0.7,0.65,0.8,0.8,0.8,0.8,0.75,0.8,0.6,0.85] unweighted_recall 0.79 [0.9,0.9,0.9,0.9,0.75,0.75,0.5,0.9,0.75,0.65] unweighted_recall 0.78 [0.75,0.75,0.65,0.8,0.75,0.9,0.8,0.9,0.75,0.75] unweighted_recall 0.82 [0.95,0.9,0.95,0.85,0.8,0.75,0.85,0.85,0.55,0.75] unweighted_recall 0.8300000000000001 [0.85,0.85,0.95,0.8,0.95,0.55,0.75,0.95,0.7,0.95] unweighted_recall 0.835 [0.9,1,0.9,0.9,0.8,0.9,0.6,0.85,0.8,0.7] usercpu_time_millis 1222.3467140001958 usercpu_time_millis 1036.3842099995964 usercpu_time_millis 1030.153612000504 usercpu_time_millis 1215.8364130000336 usercpu_time_millis 1038.7944129997777 usercpu_time_millis 1043.5352129998137 usercpu_time_millis 1396.2100210001154 usercpu_time_millis 1220.6294139996317 usercpu_time_millis 1032.5337120002587 usercpu_time_millis 1019.4027099996674 usercpu_time_millis_testing 2.207800000178395 usercpu_time_millis_testing 3.264400999796635 usercpu_time_millis_testing 2.932200000032026 usercpu_time_millis_testing 2.5562999999237945 usercpu_time_millis_testing 3.0054009994273656 usercpu_time_millis_testing 3.5674000000653905 usercpu_time_millis_testing 2.137100000254577 usercpu_time_millis_testing 2.1704000000681845 usercpu_time_millis_testing 2.8554990003613057 usercpu_time_millis_testing 2.796400000079302 usercpu_time_millis_training 1220.1389140000174 usercpu_time_millis_training 1033.1198089997997 usercpu_time_millis_training 1027.221412000472 usercpu_time_millis_training 1213.2801130001098 usercpu_time_millis_training 1035.7890120003503 usercpu_time_millis_training 1039.9678129997483 usercpu_time_millis_training 1394.0729209998608 usercpu_time_millis_training 1218.4590139995635 usercpu_time_millis_training 1029.6782129998974 usercpu_time_millis_training 1016.6063099995881 wall_clock_time_millis 1223.633050918579 wall_clock_time_millis 1040.6193733215332 wall_clock_time_millis 1030.9300422668457 wall_clock_time_millis 1233.2186698913574 wall_clock_time_millis 1039.4518375396729 wall_clock_time_millis 1045.7069873809814 wall_clock_time_millis 1398.8840579986572 wall_clock_time_millis 1220.9508419036865 wall_clock_time_millis 1033.1661701202393 wall_clock_time_millis 1171.4272499084473 wall_clock_time_millis_testing 2.2118091583251953 wall_clock_time_millis_testing 3.270387649536133 wall_clock_time_millis_testing 2.936840057373047 wall_clock_time_millis_testing 2.560853958129883 wall_clock_time_millis_testing 3.009796142578125 wall_clock_time_millis_testing 3.5712718963623047 wall_clock_time_millis_testing 2.1407604217529297 wall_clock_time_millis_testing 2.174854278564453 wall_clock_time_millis_testing 2.859354019165039 wall_clock_time_millis_testing 2.8023719787597656 wall_clock_time_millis_training 1221.421241760254 wall_clock_time_millis_training 1037.348985671997 wall_clock_time_millis_training 1027.9932022094727 wall_clock_time_millis_training 1230.6578159332275 wall_clock_time_millis_training 1036.4420413970947 wall_clock_time_millis_training 1042.1357154846191 wall_clock_time_millis_training 1396.7432975769043 wall_clock_time_millis_training 1218.775987625122 wall_clock_time_millis_training 1030.3068161010742 wall_clock_time_millis_training 1168.6248779296875 weighted_recall 0.84 [0.85,0.7,0.95,0.9,0.95,0.8,0.75,0.8,0.8,0.9] weighted_recall 0.835 [0.9,0.85,0.9,0.8,0.8,0.55,0.9,0.85,0.85,0.95] weighted_recall 0.805 [0.85,0.9,0.75,0.7,0.75,0.85,0.85,0.75,0.7,0.95] weighted_recall 0.825 [0.9,0.85,0.85,0.9,0.85,0.65,0.85,0.9,0.8,0.7] weighted_recall 0.755 [0.7,0.65,0.8,0.8,0.8,0.8,0.75,0.8,0.6,0.85] weighted_recall 0.79 [0.9,0.9,0.9,0.9,0.75,0.75,0.5,0.9,0.75,0.65] weighted_recall 0.78 [0.75,0.75,0.65,0.8,0.75,0.9,0.8,0.9,0.75,0.75] weighted_recall 0.82 [0.95,0.9,0.95,0.85,0.8,0.75,0.85,0.85,0.55,0.75] weighted_recall 0.83 [0.85,0.85,0.95,0.8,0.95,0.55,0.75,0.95,0.7,0.95] weighted_recall 0.835 [0.9,1,0.9,0.9,0.8,0.9,0.6,0.85,0.8,0.7]