10437785 11497 Fares Gaaloul 2275 Supervised Classification predictive_accuracy 17579 sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),adaboostclassifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(2) 8260895 memory null 17579 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "adaboostclassifier", "step_name": "adaboostclassifier"}}] 17579 n_jobs null 17580 remainder "passthrough" 17580 sparse_threshold 0.3 17580 transformer_weights null 17580 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}] 17580 memory null 17581 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "missingindicator", "step_name": "missingindicator"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] 17581 error_on_new false 17582 features "missing-only" 17582 missing_values NaN 17582 sparse "auto" 17582 axis 0 17583 copy true 17583 missing_values "NaN" 17583 strategy "median" 17583 verbose 0 17583 copy true 17584 with_mean true 17584 with_std true 17584 memory null 17585 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"}}] 17585 copy true 17586 fill_value -1 17586 missing_values NaN 17586 strategy "constant" 17586 verbose 0 17586 categorical_features null 17587 categories null 17587 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 17587 handle_unknown "ignore" 17587 n_values null 17587 sparse true 17587 algorithm "SAMME" 17588 learning_rate 0.8258296169139974 17588 n_estimators 429 17588 random_state 53944 17588 class_weight null 17589 criterion "gini" 17589 max_depth 3 17589 max_features null 17589 max_leaf_nodes null 17589 min_impurity_decrease 0.0 17589 min_impurity_split null 17589 min_samples_leaf 1 17589 min_samples_split 2 17589 min_weight_fraction_leaf 0.0 17589 presort false 17589 random_state 16546 17589 splitter "best" 17589 openml-python Sklearn_0.20.0. 278 meta_instanceincremental.arff https://www.openml.org/data/download/12456/meta_instanceincremental.arff -1 21799923 description https://api.openml.org/data/download/21799923/description.xml -1 21799924 predictions https://api.openml.org/data/download/21799924/predictions.arff area_under_roc_curve 0.9189535134957669 [0.870265,0.910648,0.989744,0.985915] average_cost 0 f_measure 0.8544657511762775 [0.461538,0.912281,0.875,0.8] kappa 0.6520921485660556 kb_relative_information_score -0.4430532619480587 mean_absolute_error 0.35449692181253706 mean_prior_absolute_error 0.22765072765072786 weighted_recall 0.8648648648648649 [0.375,0.962963,0.777778,0.666667] number_of_instances 74 [8,54,9,3] precision 0.8594594594594595 [0.6,0.866667,1,1] predictive_accuracy 0.8648648648648648 prior_entropy 1.238778199999325 relative_absolute_error 1.5571965241263026 root_mean_prior_squared_error 0.3317335939498282 root_mean_squared_error 0.4095752942249476 root_relative_squared_error 1.2346512433313952 total_cost 0 unweighted_recall 0.6956018518518517 [0.375,0.962963,0.777778,0.666667] area_under_roc_curve 1 [1,1,1,0.0] area_under_roc_curve 0.9196428571428571 [0.0,0.916667,1,0.857143] area_under_roc_curve 1 [0.0,1,1,1] area_under_roc_curve 0.9107142857142857 [0.785714,0.9,1,1] area_under_roc_curve 0.9523809523809524 [0.666667,1,1,0.0] area_under_roc_curve 1 [1,1,1,0.0] area_under_roc_curve 1 [1,1,1,0.0] area_under_roc_curve 1 [1,1,1,0.0] area_under_roc_curve 0.9047619047619048 [0.833333,0.9,1,0.0] area_under_roc_curve 0.8333333333333333 [0.833333,0.833333,0.0,0.0] 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 average_cost 0 f_measure 1 [1,1,1,0.0] f_measure 1 [0.0,1,1,1] f_measure 1 [1,1,1,0.0] f_measure 0.7142857142857144 [0,0.8,1,0.0] f_measure 0.7142857142857143 [0,0.833333,0.0,0.0] kappa 1 kappa 0.6190476190476191 kappa 1 kappa 0.75 kappa 0 kappa 0.6111111111111109 kappa 1 kappa 0.6111111111111109 kappa 0.36363636363636376 kappa -0.16666666666666635 kb_relative_information_score -0.5488852188592569 kb_relative_information_score -0.38703379455618764 kb_relative_information_score -0.38845548395000434 kb_relative_information_score -0.11693852287463628 kb_relative_information_score -0.4958740352697671 kb_relative_information_score -0.4522118662408287 kb_relative_information_score -0.43607446682250184 kb_relative_information_score -0.4651872526700883 kb_relative_information_score -0.4555164233196725 kb_relative_information_score -1.0301076331724714 mean_absolute_error 0.35211746019713785 mean_absolute_error 0.3521136072197948 mean_absolute_error 0.3547296943302829 mean_absolute_error 0.3537316468655298 mean_absolute_error 0.35974716020192105 mean_absolute_error 0.3534816800315678 mean_absolute_error 0.353595935283621 mean_absolute_error 0.3571805540474844 mean_absolute_error 0.3554648347311182 mean_absolute_error 0.3535631135936864 mean_prior_absolute_error 0.2203525641025641 mean_prior_absolute_error 0.22435897435897434 mean_prior_absolute_error 0.22435897435897434 mean_prior_absolute_error 0.26121794871794873 mean_prior_absolute_error 0.23076923076923078 mean_prior_absolute_error 0.23076923076923078 mean_prior_absolute_error 0.23076923076923078 mean_prior_absolute_error 0.23076923076923078 mean_prior_absolute_error 0.23076923076923078 mean_prior_absolute_error 0.18956043956043958 number_of_instances 8 [1,6,1,0] number_of_instances 8 [0,6,1,1] number_of_instances 8 [0,6,1,1] number_of_instances 8 [1,5,1,1] number_of_instances 7 [1,5,1,0] number_of_instances 7 [1,5,1,0] number_of_instances 7 [1,5,1,0] number_of_instances 7 [1,5,1,0] number_of_instances 7 [1,5,1,0] number_of_instances 7 [1,6,0,0] precision 1 [1,1,1,0.0] precision 1 [0.0,1,1,1] precision 1 [1,1,1,0.0] precision 0.7142857142857143 [0,0.8,1,0.0] precision 0.7142857142857143 [0,0.833333,0.0,0.0] predictive_accuracy 1 predictive_accuracy 0.875 predictive_accuracy 1 predictive_accuracy 0.875 predictive_accuracy 0.7142857142857143 predictive_accuracy 0.8571428571428571 predictive_accuracy 1 predictive_accuracy 0.8571428571428571 predictive_accuracy 0.7142857142857143 predictive_accuracy 0.7142857142857143 prior_entropy 1.1379007966775443 prior_entropy 1.2841414218578335 prior_entropy 1.2841414218578335 prior_entropy 1.6105707608681268 prior_entropy 1.2284519811546808 prior_entropy 1.2284519811546808 prior_entropy 1.2284519811546808 prior_entropy 1.2284519811546808 prior_entropy 1.2284519811546808 prior_entropy 0.8771046070636382 relative_absolute_error 1.5979730557310112 relative_absolute_error 1.569420649322514 relative_absolute_error 1.5810809233006895 relative_absolute_error 1.3541628689821508 relative_absolute_error 1.5589043608749913 relative_absolute_error 1.5317539468034604 relative_absolute_error 1.5322490528956907 relative_absolute_error 1.5477824008724324 relative_absolute_error 1.540347617168179 relative_absolute_error 1.8651735267840845 root_mean_prior_squared_error 0.32054487019246797 root_mean_prior_squared_error 0.3267344855737092 root_mean_prior_squared_error 0.3267344855737092 root_mean_prior_squared_error 0.3789649039450629 root_mean_prior_squared_error 0.33640107085645965 root_mean_prior_squared_error 0.33640107085645965 root_mean_prior_squared_error 0.33640107085645965 root_mean_prior_squared_error 0.33640107085645965 root_mean_prior_squared_error 0.33640107085645965 root_mean_prior_squared_error 0.26824781315899215 root_mean_squared_error 0.4066278166255955 root_mean_squared_error 0.4069350389753289 root_mean_squared_error 0.4096765032719036 root_mean_squared_error 0.408949218848967 root_mean_squared_error 0.41574058542614045 root_mean_squared_error 0.4084002717369861 root_mean_squared_error 0.40836557637911014 root_mean_squared_error 0.412642420277675 root_mean_squared_error 0.41068535532470535 root_mean_squared_error 0.40851674516614644 root_relative_squared_error 1.2685519390200815 root_relative_squared_error 1.2454609382930502 root_relative_squared_error 1.25385143399405 root_relative_squared_error 1.0791216141435906 root_relative_squared_error 1.235847984572958 root_relative_squared_error 1.2140278587616273 root_relative_squared_error 1.2139247218786569 root_relative_squared_error 1.2266382482882972 root_relative_squared_error 1.2208205945335482 root_relative_squared_error 1.5229080168643014 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 [0,1,1,1] usercpu_time_millis 383.3196590000014 usercpu_time_millis 95.18873399999705 usercpu_time_millis 741.9232870000022 usercpu_time_millis 11.594927000000865 usercpu_time_millis 84.75057199999725 usercpu_time_millis 139.29700799999978 usercpu_time_millis 117.16133599999878 usercpu_time_millis 236.36414099999925 usercpu_time_millis 753.5183879999962 usercpu_time_millis 87.30232999999998 usercpu_time_millis_testing 11.207319000000382 usercpu_time_millis_testing 3.3260149999989608 usercpu_time_millis_testing 21.517560999999574 usercpu_time_millis_testing 0.8917450000005545 usercpu_time_millis_testing 3.0464359999982094 usercpu_time_millis_testing 4.190922999999458 usercpu_time_millis_testing 3.9945119999984513 usercpu_time_millis_testing 7.17024700000124 usercpu_time_millis_testing 22.195238999998423 usercpu_time_millis_testing 3.108826999998371 usercpu_time_millis_training 372.11234000000104 usercpu_time_millis_training 91.8627189999981 usercpu_time_millis_training 720.4057260000027 usercpu_time_millis_training 10.70318200000031 usercpu_time_millis_training 81.70413599999904 usercpu_time_millis_training 135.10608500000032 usercpu_time_millis_training 113.16682400000033 usercpu_time_millis_training 229.193893999998 usercpu_time_millis_training 731.3231489999978 usercpu_time_millis_training 84.19350300000161 wall_clock_time_millis 383.5015296936035 wall_clock_time_millis 95.54815292358398 wall_clock_time_millis 742.0673370361328 wall_clock_time_millis 11.262178421020508 wall_clock_time_millis 84.48028564453125 wall_clock_time_millis 138.54503631591797 wall_clock_time_millis 117.08831787109375 wall_clock_time_millis 235.3382110595703 wall_clock_time_millis 755.0761699676514 wall_clock_time_millis 87.56089210510254 wall_clock_time_millis_testing 11.214971542358398 wall_clock_time_millis_testing 3.329753875732422 wall_clock_time_millis_testing 21.629810333251953 wall_clock_time_millis_testing 0.8943080902099609 wall_clock_time_millis_testing 3.050088882446289 wall_clock_time_millis_testing 4.195451736450195 wall_clock_time_millis_testing 3.9992332458496094 wall_clock_time_millis_testing 7.177591323852539 wall_clock_time_millis_testing 22.478580474853516 wall_clock_time_millis_testing 3.11279296875 wall_clock_time_millis_training 372.2865581512451 wall_clock_time_millis_training 92.21839904785156 wall_clock_time_millis_training 720.4375267028809 wall_clock_time_millis_training 10.367870330810547 wall_clock_time_millis_training 81.43019676208496 wall_clock_time_millis_training 134.34958457946777 wall_clock_time_millis_training 113.08908462524414 wall_clock_time_millis_training 228.16061973571777 wall_clock_time_millis_training 732.5975894927979 wall_clock_time_millis_training 84.44809913635254 weighted_recall 1 [1,1,1,0.0] weighted_recall 0.875 [0.0,1,1,0] weighted_recall 1 [0.0,1,1,1] weighted_recall 0.875 [0,1,1,1] weighted_recall 0.7142857142857143 [0,1,0,0.0] weighted_recall 0.8571428571428571 [0,1,1,0.0] weighted_recall 1 [1,1,1,0.0] weighted_recall 0.8571428571428571 [1,1,0,0.0] weighted_recall 0.7142857142857143 [0,0.8,1,0.0] weighted_recall 0.7142857142857143 [0,0.833333,0.0,0.0]