10397108 8323 Heinrich Peters 9977 Supervised Classification 16374 sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC)(2) 8235640 add_indicator false 12737 copy true 12737 fill_value null 12737 missing_values NaN 12737 strategy "most_frequent" 12737 verbose 0 12737 copy true 13294 with_mean true 13294 with_std true 13294 C 0.6272435651551626 13389 cache_size 200 13389 class_weight null 13389 coef0 0.28238925509690915 13389 decision_function_shape "ovr" 13389 degree 1 13389 gamma 0.004979268646665967 13389 kernel "rbf" 13389 max_iter -1 13389 probability false 13389 random_state 1 13389 shrinking true 13389 tol 0.001 13389 verbose false 13389 categorical_features null 16348 categories null 16348 drop null 16348 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 16348 handle_unknown "ignore" 16348 n_values null 16348 sparse true 16348 memory null 16374 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}] 16374 verbose false 16374 n_jobs null 16375 remainder "drop" 16375 sparse_threshold 0.3 16375 transformer_weights null 16375 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "num", "step_name": "num", "argument_1": [true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, false, true, true, true, false, true, true, true, false, true, true, true, false, true, true, true, false, true, true, true, false, true, true, true, false, true, true]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, true, false, false, false, true, false, false, false, true, false, false, false, true, false, false, false, true, false, false, false, true, false, false, false, true, false, false]}}] 16375 verbose false 16375 memory null 16376 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] 16376 verbose false 16376 memory null 16377 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}] 16377 verbose false 16377 openml-python Sklearn_0.21.2. 1486 nomao https://www.openml.org/data/download/1592278/phpDYCOet -1 21715959 description https://api.openml.org/data/download/21715959/description.xml -1 21715960 predictions https://api.openml.org/data/download/21715960/predictions.arff area_under_roc_curve 0.9452722245264211 [0.945272,0.945272] average_cost 0 f_measure 0.9569933888992063 [0.924336,0.97005] kappa 0.8943888126428674 kb_relative_information_score 0.885978429867847 mean_absolute_error 0.04291310024662701 mean_prior_absolute_error 0.4080904194746198 number_of_instances 34465 [9844,24621] precision 0.9569386530305711 [0.931052,0.967289] predictive_accuracy 0.9570868997533729 prior_entropy 0.8629999933345911 recall 0.957086899753373 [0.917716,0.972828] relative_absolute_error 0.10515586301161842 root_mean_prior_squared_error 0.4517106852187937 root_mean_squared_error 0.20715477365155507 root_relative_squared_error 0.45860056100115537 total_cost 0 area_under_roc_curve 0.9438178950912529 [0.943818,0.943818] area_under_roc_curve 0.9470021483915927 [0.947002,0.947002] area_under_roc_curve 0.941823741170358 [0.941824,0.941824] area_under_roc_curve 0.9500478336707805 [0.950048,0.950048] area_under_roc_curve 0.9464949465376257 [0.946495,0.946495] area_under_roc_curve 0.9408629047703962 [0.940863,0.940863] area_under_roc_curve 0.9494024621399749 [0.949402,0.949402] area_under_roc_curve 0.9469612913025963 [0.946961,0.946961] area_under_roc_curve 0.9465542919036014 [0.946554,0.946554] area_under_roc_curve 0.9397455139254886 [0.939746,0.939746] 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 0.9558081985965073 [0.922211,0.969231] f_measure 0.9573215943815665 [0.925191,0.970177] f_measure 0.9513354517706128 [0.915152,0.965812] f_measure 0.9591010873539596 [0.928463,0.971359] f_measure 0.9586768238178921 [0.927179,0.971278] f_measure 0.9562094257798923 [0.92228,0.96977] f_measure 0.958237814288851 [0.926978,0.970732] f_measure 0.9589621920347811 [0.927655,0.971475] f_measure 0.9592209325574063 [0.927984,0.971706] f_measure 0.9550563227971697 [0.92029,0.968952] kappa 0.8914438230113139 kappa 0.895367616160636 kappa 0.8809647043830205 kappa 0.8998216470920932 kappa 0.898462017176285 kappa 0.8920663212677581 kappa 0.8977095636313863 kappa 0.8991338003053269 kappa 0.8996952852376343 kappa 0.8892564453147825 kb_relative_information_score 0.8828063054270265 kb_relative_information_score 0.8867117377121478 kb_relative_information_score 0.8705277002424512 kb_relative_information_score 0.8913357484177736 kb_relative_information_score 0.8905650799668353 kb_relative_information_score 0.884329454026971 kb_relative_information_score 0.8889562758658924 kb_relative_information_score 0.8912696867853529 kb_relative_information_score 0.8920408237585068 kb_relative_information_score 0.8812449061343577 mean_absolute_error 0.04409631563678561 mean_absolute_error 0.04264577893820714 mean_absolute_error 0.04873803307223673 mean_absolute_error 0.040905134899912966 mean_absolute_error 0.041195242239628664 mean_absolute_error 0.04352872896111434 mean_absolute_error 0.04178757980266976 mean_absolute_error 0.040917005223447475 mean_absolute_error 0.04062681369704005 mean_absolute_error 0.04468949506674405 mean_prior_absolute_error 0.4080229059806574 mean_prior_absolute_error 0.4081472833897437 mean_prior_absolute_error 0.4081472833897437 mean_prior_absolute_error 0.4081472833897437 mean_prior_absolute_error 0.4081472833897437 mean_prior_absolute_error 0.40805842175852763 mean_prior_absolute_error 0.40805842175852763 mean_prior_absolute_error 0.40805842175852763 mean_prior_absolute_error 0.40805842175852763 mean_prior_absolute_error 0.40805842175852763 number_of_instances 3447 [984,2463] number_of_instances 3447 [985,2462] number_of_instances 3447 [985,2462] number_of_instances 3447 [985,2462] number_of_instances 3447 [985,2462] number_of_instances 3446 [984,2462] number_of_instances 3446 [984,2462] number_of_instances 3446 [984,2462] number_of_instances 3446 [984,2462] number_of_instances 3446 [984,2462] precision 0.9557508672983193 [0.928866,0.966492] precision 0.9572938173312423 [0.927551,0.969193] precision 0.9514279914102618 [0.910553,0.967781] precision 0.959107503449819 [0.927992,0.971556] precision 0.9586273428289166 [0.936788,0.967365] precision 0.9562331191355852 [0.940803,0.9624] precision 0.9582663033635181 [0.925101,0.971522] precision 0.9589122518623306 [0.936788,0.967755] precision 0.9591821338205501 [0.939583,0.967015] precision 0.9550576288296886 [0.937764,0.96197] predictive_accuracy 0.9559036843632144 predictive_accuracy 0.9573542210617929 predictive_accuracy 0.9512619669277633 predictive_accuracy 0.959094865100087 predictive_accuracy 0.9588047577603713 predictive_accuracy 0.9564712710388856 predictive_accuracy 0.9582124201973302 predictive_accuracy 0.9590829947765525 predictive_accuracy 0.9593731863029599 predictive_accuracy 0.955310504933256 prior_entropy 0.862791736866817 prior_entropy 0.863175399426382 prior_entropy 0.863175399426382 prior_entropy 0.863175399426382 prior_entropy 0.863175399426382 prior_entropy 0.862901291120622 prior_entropy 0.862901291120622 prior_entropy 0.862901291120622 prior_entropy 0.862901291120622 prior_entropy 0.862901291120622 recall 0.9559036843632144 [0.91565,0.971985] recall 0.9573542210617929 [0.922843,0.971162] recall 0.9512619669277633 [0.919797,0.963851] recall 0.959094865100087 [0.928934,0.971162] recall 0.9588047577603713 [0.917766,0.975223] recall 0.9564712710388856 [0.904472,0.977254] recall 0.9582124201973302 [0.928862,0.969943] recall 0.9590829947765526 [0.918699,0.975223] recall 0.95937318630296 [0.916667,0.976442] recall 0.955310504933256 [0.903455,0.976036] relative_absolute_error 0.10807313753820484 relative_absolute_error 0.10448624963034307 relative_absolute_error 0.11941285672039209 relative_absolute_error 0.10022150474747192 relative_absolute_error 0.10093229556128377 relative_absolute_error 0.106672786640519 relative_absolute_error 0.10240587517489824 relative_absolute_error 0.10027241944208785 relative_absolute_error 0.09956126753115108 relative_absolute_error 0.10951739428426617 root_mean_prior_squared_error 0.4516359481339797 root_mean_prior_squared_error 0.45177362368332785 root_mean_prior_squared_error 0.45177362368332785 root_mean_prior_squared_error 0.45177362368332785 root_mean_prior_squared_error 0.45177362368332785 root_mean_prior_squared_error 0.4516752654560011 root_mean_prior_squared_error 0.4516752654560011 root_mean_prior_squared_error 0.4516752654560011 root_mean_prior_squared_error 0.4516752654560011 root_mean_prior_squared_error 0.4516752654560011 root_mean_squared_error 0.20999122752340302 root_mean_squared_error 0.2065085444677947 root_mean_squared_error 0.2207669202399597 root_mean_squared_error 0.20225017898610861 root_mean_squared_error 0.2029661110619915 root_mean_squared_error 0.20863539719116297 root_mean_squared_error 0.2044201061604992 root_mean_squared_error 0.20227952250153122 root_mean_squared_error 0.20156094288586776 root_mean_squared_error 0.21139890034421666 root_relative_squared_error 0.4649568494071872 root_relative_squared_error 0.457106244459609 root_relative_squared_error 0.4886671303207975 root_relative_squared_error 0.4476803611002233 root_relative_squared_error 0.4492650752985554 root_relative_squared_error 0.4619145947266548 root_relative_squared_error 0.4525820247299158 root_relative_squared_error 0.44784281534060627 root_relative_squared_error 0.44625189444982427 root_relative_squared_error 0.4680329354116682 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 usercpu_time_millis 64973.379590999684 usercpu_time_millis 70653.32488698186 usercpu_time_millis 67970.18500400009 usercpu_time_millis 65745.70258200401 usercpu_time_millis 72217.7902830008 usercpu_time_millis 60123.86234600854 usercpu_time_millis 58202.86919698992 usercpu_time_millis 59541.700608999236 usercpu_time_millis 69908.41003399692 usercpu_time_millis 72057.19580699224 usercpu_time_millis_testing 4503.062402000069 usercpu_time_millis_testing 6377.000052991207 usercpu_time_millis_testing 4454.450468998402 usercpu_time_millis_testing 6421.512340006302 usercpu_time_millis_testing 6568.464252995909 usercpu_time_millis_testing 4452.08549000381 usercpu_time_millis_testing 4451.773245993536 usercpu_time_millis_testing 6410.905199998524 usercpu_time_millis_testing 6395.470736999414 usercpu_time_millis_testing 6475.302919003298 usercpu_time_millis_training 60470.317188999616 usercpu_time_millis_training 64276.32483399066 usercpu_time_millis_training 63515.734535001684 usercpu_time_millis_training 59324.19024199771 usercpu_time_millis_training 65649.32603000489 usercpu_time_millis_training 55671.776856004726 usercpu_time_millis_training 53751.09595099639 usercpu_time_millis_training 53130.79540900071 usercpu_time_millis_training 63512.9392969975 usercpu_time_millis_training 65581.89288798894 wall_clock_time_millis 64979.87604141235 wall_clock_time_millis 70654.19816970825 wall_clock_time_millis 67971.87757492065 wall_clock_time_millis 65749.47571754456 wall_clock_time_millis 72218.2309627533 wall_clock_time_millis 60124.44591522217 wall_clock_time_millis 58203.38249206543 wall_clock_time_millis 59542.147159576416 wall_clock_time_millis 69910.02631187439 wall_clock_time_millis 72061.05399131775 wall_clock_time_millis_testing 4503.117084503174 wall_clock_time_millis_testing 6377.1302700042725 wall_clock_time_millis_testing 4454.789876937866 wall_clock_time_millis_testing 6421.571493148804 wall_clock_time_millis_testing 6568.511486053467 wall_clock_time_millis_testing 4452.122211456299 wall_clock_time_millis_testing 4451.824188232422 wall_clock_time_millis_testing 6410.9532833099365 wall_clock_time_millis_testing 6395.512580871582 wall_clock_time_millis_testing 6475.380420684814 wall_clock_time_millis_training 60476.75895690918 wall_clock_time_millis_training 64277.06789970398 wall_clock_time_millis_training 63517.08769798279 wall_clock_time_millis_training 59327.90422439575 wall_clock_time_millis_training 65649.71947669983 wall_clock_time_millis_training 55672.32370376587 wall_clock_time_millis_training 53751.55830383301 wall_clock_time_millis_training 53131.19387626648 wall_clock_time_millis_training 63514.51373100281 wall_clock_time_millis_training 65585.67357063293