10576529 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) 8292811 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 "most_frequent" 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 0.9118320230516977 19038 loss "deviance" 19038 max_depth 3 19038 max_features null 19038 max_leaf_nodes 914 19038 min_impurity_decrease 0.0 19038 min_impurity_split null 19038 min_samples_leaf 140 19038 min_samples_split 2 19038 min_weight_fraction_leaf 0.0 19038 n_estimators 100 19038 n_iter_no_change 11 19038 random_state 29961 19038 subsample 1.0 19038 tol 0.0001 19038 validation_fraction 0.08262384105539997 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 22079325 description https://api.openml.org/data/download/22079325/description.xml -1 22079326 predictions https://api.openml.org/data/download/22079326/predictions.arff area_under_roc_curve 0.8429338888888889 [0.846233,0.882911,0.884681,0.918889,0.965818,0.925433,0.963561,0.368822,0.923253,0.749737] average_cost 0 f_measure 0.7500471049652975 [0.812665,0.710817,0.82963,0.775056,0.874036,0.811224,0.879177,0.377246,0.805333,0.625287] kappa 0.726111111111111 kb_relative_information_score 0.7475909509122382 mean_absolute_error 0.051532362141681026 mean_prior_absolute_error 0.18000000000000554 weighted_recall 0.7535 [0.77,0.805,0.84,0.87,0.85,0.795,0.855,0.315,0.755,0.68] number_of_instances 2000 [200,200,200,200,200,200,200,200,200,200] precision 0.7559093812815816 [0.860335,0.636364,0.819512,0.698795,0.899471,0.828125,0.904762,0.470149,0.862857,0.578723] predictive_accuracy 0.7535 prior_entropy 3.3219280948872383 relative_absolute_error 0.286290900787108 root_mean_prior_squared_error 0.3000000000000046 root_mean_squared_error 0.21244297746841714 root_relative_squared_error 0.7081432582280462 total_cost 0 unweighted_recall 0.7535000000000001 [0.77,0.805,0.84,0.87,0.85,0.795,0.855,0.315,0.755,0.68] area_under_roc_curve 0.8813333333333334 [0.985556,0.839167,1,0.912222,0.988056,0.905278,0.998889,0.293611,0.945556,0.945] area_under_roc_curve 0.7862916666666666 [0.55,0.805278,0.999444,0.921111,0.892222,0.792639,0.993333,0.528611,0.875,0.505278] area_under_roc_curve 0.6813333333333332 [0.494722,0.789167,0.440833,0.96,0.899722,0.991111,0.835556,0.195556,0.715,0.491667] area_under_roc_curve 0.816263888888889 [0.678056,0.939444,0.996667,0.970556,0.959722,0.906944,0.998333,0.153194,0.99,0.569722] area_under_roc_curve 0.8603888888888889 [1,0.862222,0.996389,0.902778,0.981389,0.953889,0.970833,0.314722,0.975,0.646667] area_under_roc_curve 0.9113055555555555 [1,0.997778,1,0.91,0.997778,0.982778,0.985556,0.301389,0.993889,0.943889] area_under_roc_curve 0.9078055555555556 [0.996944,0.946667,0.995833,0.908056,0.999167,0.996667,0.999167,0.407222,0.978056,0.850278] area_under_roc_curve 0.9084027777777777 [1,0.937361,0.998889,0.997222,0.999444,0.980278,0.997222,0.300278,0.959722,0.913611] area_under_roc_curve 0.7676805555555555 [0.909167,0.790139,0.549444,0.7425,0.996944,0.785556,0.936667,0.293333,0.914167,0.758889] area_under_roc_curve 0.9941666666666668 [1,0.997778,1,1,0.995833,0.999167,0.958611,1,0.993611,0.996667] 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 0.7458052549515963 [0.918919,0.545455,0.974359,0.666667,0.820513,0.820513,0.926829,0.214286,0.75,0.820513] f_measure 0.6601649513639245 [0.486486,0.5,0.926829,0.64,0.736842,0.648649,0.923077,0.555556,0.684211,0.5] f_measure 0.5787279801916025 [0.4375,0.764706,0.311111,0.947368,0.789474,0.810811,0.764706,0.070175,0.571429,0.32] f_measure 0.7402383392029147 [0.615385,0.85,0.926829,0.974359,0.85,0.842105,0.926829,0.054054,0.894737,0.468085] f_measure 0.7634424022056061 [1,0.580645,0.947368,0.842105,0.888889,0.734694,0.894737,0.344828,0.820513,0.580645] f_measure 0.8345995959268789 [0.930233,0.926829,1,0.690909,0.926829,0.878049,0.888889,0.37037,0.923077,0.810811] f_measure 0.8237260480315082 [0.857143,0.864865,0.926829,0.654545,0.947368,0.930233,0.904762,0.533333,0.818182,0.8] f_measure 0.8048075843711275 [0.97561,0.761905,0.947368,0.9,0.909091,0.833333,0.926829,0.4,0.727273,0.666667] f_measure 0.6552940117490202 [0.833333,0.576923,0.428571,0.666667,0.918919,0.666667,0.736842,0.32,0.888889,0.516129] f_measure 0.9452268741100576 [0.974359,0.952381,1,0.909091,0.947368,0.923077,0.871795,1,0.926829,0.947368] kappa 0.7222222222222222 kappa 0.6222222222222222 kappa 0.48888888888888893 kappa 0.7111111111111111 kappa 0.7388888888888889 kappa 0.8277777777777777 kappa 0.8055555555555555 kappa 0.7944444444444444 kappa 0.6111111111111112 kappa 0.9388888888888889 kb_relative_information_score 0.7520194237880231 kb_relative_information_score 0.6574061287195443 kb_relative_information_score 0.5236982497472971 kb_relative_information_score 0.733854776933442 kb_relative_information_score 0.7533082314857082 kb_relative_information_score 0.8424040700234946 kb_relative_information_score 0.8143328037291991 kb_relative_information_score 0.8086178189386863 kb_relative_information_score 0.641236880632985 kb_relative_information_score 0.9490311251237774 mean_absolute_error 0.050374538011306196 mean_absolute_error 0.06745303440258937 mean_absolute_error 0.09296996135740476 mean_absolute_error 0.05463024179175891 mean_absolute_error 0.04969777298197694 mean_absolute_error 0.035514167641663236 mean_absolute_error 0.042891892192479866 mean_absolute_error 0.0387296356257734 mean_absolute_error 0.07129801712954743 mean_absolute_error 0.011764360282306884 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 0.778303621958121 [1,0.428571,1,0.548387,0.842105,0.842105,0.904762,0.375,1,0.842105] precision 0.6736829205366357 [0.529412,0.428571,0.904762,0.533333,0.777778,0.705882,0.947368,0.625,0.722222,0.5625] precision 0.6423549852373381 [0.583333,0.928571,0.28,1,0.833333,0.882353,0.928571,0.054054,0.666667,0.266667] precision 0.7440667027044736 [0.631579,0.85,0.904762,1,0.85,0.888889,0.904762,0.058824,0.944444,0.407407] precision 0.8098437053972444 [1,0.428571,1,0.888889,1,0.62069,0.944444,0.555556,0.842105,0.818182] precision 0.862309610342993 [0.869565,0.904762,1,0.542857,0.904762,0.857143,1,0.714286,0.947368,0.882353] precision 0.8576759003996856 [1,0.941176,0.904762,0.514286,1,0.869565,0.863636,0.8,0.75,0.933333] precision 0.867832584082584 [0.952381,0.727273,1,0.9,0.833333,0.9375,0.904762,1,0.923077,0.5] precision 0.7304435440290703 [0.9375,0.46875,0.409091,0.684211,1,0.846154,0.777778,0.8,1,0.380952] precision 0.9489291410344042 [1,0.909091,1,0.833333,1,0.947368,0.894737,1,0.904762,1] predictive_accuracy 0.75 predictive_accuracy 0.66 predictive_accuracy 0.54 predictive_accuracy 0.74 predictive_accuracy 0.765 predictive_accuracy 0.845 predictive_accuracy 0.825 predictive_accuracy 0.815 predictive_accuracy 0.65 predictive_accuracy 0.945 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 0.27985854450725695 relative_absolute_error 0.37473908001438583 relative_absolute_error 0.5164997853189159 relative_absolute_error 0.30350134328754985 relative_absolute_error 0.27609873878876107 relative_absolute_error 0.19730093134257376 relative_absolute_error 0.23828828995822174 relative_absolute_error 0.21516464236540803 relative_absolute_error 0.3961000951641529 relative_absolute_error 0.06535755712392721 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 0.21049237075654864 root_mean_squared_error 0.2508669507607512 root_mean_squared_error 0.2981960381195091 root_mean_squared_error 0.21885115818633674 root_mean_squared_error 0.2094735123335237 root_mean_squared_error 0.1631417499329979 root_mean_squared_error 0.1756174393916365 root_mean_squared_error 0.18024855849988058 root_mean_squared_error 0.2550393993645305 root_mean_squared_error 0.0916062716605889 root_relative_squared_error 0.7016412358551627 root_relative_squared_error 0.8362231692025045 root_relative_squared_error 0.9939867937316977 root_relative_squared_error 0.729503860621123 root_relative_squared_error 0.6982450411117462 root_relative_squared_error 0.5438058331099933 root_relative_squared_error 0.5853914646387887 root_relative_squared_error 0.6008285283329355 root_relative_squared_error 0.8501313312151022 root_relative_squared_error 0.3053542388686299 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 0.75 [0.85,0.75,0.95,0.85,0.8,0.8,0.95,0.15,0.6,0.8] unweighted_recall 0.66 [0.45,0.6,0.95,0.8,0.7,0.6,0.9,0.5,0.65,0.45] unweighted_recall 0.54 [0.35,0.65,0.35,0.9,0.75,0.75,0.65,0.1,0.5,0.4] unweighted_recall 0.7399999999999999 [0.6,0.85,0.95,0.95,0.85,0.8,0.95,0.05,0.85,0.55] unweighted_recall 0.7649999999999999 [1,0.9,0.9,0.8,0.8,0.9,0.85,0.25,0.8,0.45] unweighted_recall 0.8450000000000001 [1,0.95,1,0.95,0.95,0.9,0.8,0.25,0.9,0.75] unweighted_recall 0.825 [0.75,0.8,0.95,0.9,0.9,1,0.95,0.4,0.9,0.7] unweighted_recall 0.8149999999999998 [1,0.8,0.9,0.9,1,0.75,0.95,0.25,0.6,1] unweighted_recall 0.65 [0.75,0.75,0.45,0.65,0.85,0.55,0.7,0.2,0.8,0.8] unweighted_recall 0.9450000000000001 [0.95,1,1,1,0.9,0.9,0.85,1,0.95,0.9] usercpu_time_millis 3947.0656490011606 usercpu_time_millis 3898.215350999635 usercpu_time_millis 2557.544734001567 usercpu_time_millis 2603.803436999442 usercpu_time_millis 3898.4377479991963 usercpu_time_millis 2598.8992340007826 usercpu_time_millis 2593.7771310000244 usercpu_time_millis 2557.6985320003587 usercpu_time_millis 2748.53373499991 usercpu_time_millis 5258.079264998742 usercpu_time_millis_testing 2.8154000010545133 usercpu_time_millis_testing 2.8926009999850066 usercpu_time_millis_testing 3.033500001038192 usercpu_time_millis_testing 3.3910000001924345 usercpu_time_millis_testing 2.284199999849079 usercpu_time_millis_testing 2.980100000058883 usercpu_time_millis_testing 3.444900999966194 usercpu_time_millis_testing 3.0977999995229766 usercpu_time_millis_testing 2.7550009999686154 usercpu_time_millis_testing 2.669699999387376 usercpu_time_millis_training 3944.250249000106 usercpu_time_millis_training 3895.32274999965 usercpu_time_millis_training 2554.511234000529 usercpu_time_millis_training 2600.4124369992496 usercpu_time_millis_training 3896.153547999347 usercpu_time_millis_training 2595.9191340007237 usercpu_time_millis_training 2590.332230000058 usercpu_time_millis_training 2554.6007320008357 usercpu_time_millis_training 2745.7787339999413 usercpu_time_millis_training 5255.409564999354 wall_clock_time_millis 3950.6704807281494 wall_clock_time_millis 3899.702310562134 wall_clock_time_millis 2558.8226318359375 wall_clock_time_millis 2617.755889892578 wall_clock_time_millis 3901.1409282684326 wall_clock_time_millis 2601.1691093444824 wall_clock_time_millis 2600.8996963500977 wall_clock_time_millis 2560.610294342041 wall_clock_time_millis 2751.5788078308105 wall_clock_time_millis 5266.014575958252 wall_clock_time_millis_testing 2.819061279296875 wall_clock_time_millis_testing 2.897024154663086 wall_clock_time_millis_testing 3.039121627807617 wall_clock_time_millis_testing 3.3965110778808594 wall_clock_time_millis_testing 2.2878646850585938 wall_clock_time_millis_testing 2.9823780059814453 wall_clock_time_millis_testing 3.451108932495117 wall_clock_time_millis_testing 3.1027793884277344 wall_clock_time_millis_testing 2.760648727416992 wall_clock_time_millis_testing 2.672433853149414 wall_clock_time_millis_training 3947.8514194488525 wall_clock_time_millis_training 3896.8052864074707 wall_clock_time_millis_training 2555.78351020813 wall_clock_time_millis_training 2614.3593788146973 wall_clock_time_millis_training 3898.853063583374 wall_clock_time_millis_training 2598.186731338501 wall_clock_time_millis_training 2597.4485874176025 wall_clock_time_millis_training 2557.5075149536133 wall_clock_time_millis_training 2748.8181591033936 wall_clock_time_millis_training 5263.3421421051025 weighted_recall 0.75 [0.85,0.75,0.95,0.85,0.8,0.8,0.95,0.15,0.6,0.8] weighted_recall 0.66 [0.45,0.6,0.95,0.8,0.7,0.6,0.9,0.5,0.65,0.45] weighted_recall 0.54 [0.35,0.65,0.35,0.9,0.75,0.75,0.65,0.1,0.5,0.4] weighted_recall 0.74 [0.6,0.85,0.95,0.95,0.85,0.8,0.95,0.05,0.85,0.55] weighted_recall 0.765 [1,0.9,0.9,0.8,0.8,0.9,0.85,0.25,0.8,0.45] weighted_recall 0.845 [1,0.95,1,0.95,0.95,0.9,0.8,0.25,0.9,0.75] weighted_recall 0.825 [0.75,0.8,0.95,0.9,0.9,1,0.95,0.4,0.9,0.7] weighted_recall 0.815 [1,0.8,0.9,0.9,1,0.75,0.95,0.25,0.6,1] weighted_recall 0.65 [0.75,0.75,0.45,0.65,0.85,0.55,0.7,0.2,0.8,0.8] weighted_recall 0.945 [0.95,1,1,1,0.9,0.9,0.85,1,0.95,0.9]