10397101 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) 8235633 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 20.637991636404358 13389 cache_size 200 13389 class_weight null 13389 coef0 0.7270032659013304 13389 decision_function_shape "ovr" 13389 degree 5 13389 gamma 3.268409654294397 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 21715945 description https://api.openml.org/data/download/21715945/description.xml -1 21715946 predictions https://api.openml.org/data/download/21715946/predictions.arff area_under_roc_curve 0.6003869205716154 [0.600387,0.600387] average_cost 0 f_measure 0.7112761909385076 [0.337152,0.860859] kappa 0.26266050407175 kb_relative_information_score 0.38887830531617973 mean_absolute_error 0.23000145074713477 mean_prior_absolute_error 0.4080904194746198 number_of_instances 34465 [9844,24621] precision 0.8137666554074618 [0.953191,0.758022] predictive_accuracy 0.7699985492528653 prior_entropy 0.8629999933345911 recall 0.7699985492528653 [0.204795,0.995979] relative_absolute_error 0.5636041420507769 root_mean_prior_squared_error 0.4517106852187937 root_mean_squared_error 0.47958466483733064 root_relative_squared_error 1.061707594109791 total_cost 0 area_under_roc_curve 0.5943116663200737 [0.594312,0.594312] area_under_roc_curve 0.6039590609755595 [0.603959,0.603959] area_under_roc_curve 0.5904563579608011 [0.590456,0.590456] area_under_roc_curve 0.6041619417171464 [0.604162,0.604162] area_under_roc_curve 0.5989845653939885 [0.598985,0.598985] area_under_roc_curve 0.5967494534815372 [0.596749,0.596749] area_under_roc_curve 0.6086411833858387 [0.608641,0.608641] area_under_roc_curve 0.6020338412157477 [0.602034,0.602034] area_under_roc_curve 0.5974606704840404 [0.597461,0.597461] area_under_roc_curve 0.6071151420287558 [0.607115,0.607115] 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 0.7053228690604534 [0.320675,0.858995] f_measure 0.7147376061110015 [0.346378,0.862111] f_measure 0.7011277959619144 [0.310169,0.857543] f_measure 0.7148374821825985 [0.34797,0.861614] f_measure 0.70984916364101 [0.331929,0.861048] f_measure 0.7077292560957243 [0.326874,0.859947] f_measure 0.7192228698273658 [0.361406,0.862233] f_measure 0.7130235666467679 [0.341137,0.861657] f_measure 0.7084608474446712 [0.328559,0.860298] f_measure 0.718033990071234 [0.354892,0.863173] kappa 0.24783424371131718 kappa 0.27149790527328377 kappa 0.23822377155330376 kappa 0.271380260309106 kappa 0.2599537747089667 kappa 0.2540151475332021 kappa 0.2811832784648725 kappa 0.26692727174558617 kappa 0.25588244378468333 kappa 0.27910663676599506 kb_relative_information_score 0.3793360254523275 kb_relative_information_score 0.39502526601383464 kb_relative_information_score 0.37267588093663834 kb_relative_information_score 0.39348392911195873 kb_relative_information_score 0.3888599184063322 kb_relative_information_score 0.38386155845032255 kb_relative_information_score 0.39774202396708636 kb_relative_information_score 0.3923440651550117 kb_relative_information_score 0.38540383239662995 kb_relative_information_score 0.4000554348865471 mean_absolute_error 0.23353640847113433 mean_absolute_error 0.22773426167682043 mean_absolute_error 0.23614737452857557 mean_absolute_error 0.22831447635625182 mean_absolute_error 0.23005512039454598 mean_absolute_error 0.2318630295995357 mean_absolute_error 0.22663958212420196 mean_absolute_error 0.228670922809054 mean_absolute_error 0.23128264654672084 mean_absolute_error 0.2257690075449797 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 0.8095961428425568 [0.945274,0.755391] precision 0.8176529762519921 [0.962963,0.759517] precision 0.8062699198324914 [0.938462,0.753383] precision 0.8129136483620667 [0.945946,0.75969] precision 0.8194832374329172 [0.975248,0.757165] precision 0.8132986757207512 [0.955665,0.756398] precision 0.808514051200292 [0.924686,0.762083] precision 0.8169078888597436 [0.962264,0.758813] precision 0.81492562413778 [0.960591,0.756707] precision 0.8190748275325064 [0.963964,0.761166] predictive_accuracy 0.7664635915288658 predictive_accuracy 0.7722657383231796 predictive_accuracy 0.7638526254714244 predictive_accuracy 0.7716855236437482 predictive_accuracy 0.769944879605454 predictive_accuracy 0.7681369704004644 predictive_accuracy 0.773360417875798 predictive_accuracy 0.771329077190946 predictive_accuracy 0.7687173534532792 predictive_accuracy 0.7742309924550203 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 0.7664635915288657 [0.193089,0.995534] recall 0.7722657383231796 [0.211168,0.996751] recall 0.7638526254714244 [0.185787,0.995126] recall 0.7716855236437482 [0.213198,0.995126] recall 0.769944879605454 [0.2,0.997969] recall 0.7681369704004644 [0.197154,0.996344] recall 0.773360417875798 [0.224593,0.992689] recall 0.771329077190946 [0.207317,0.996751] recall 0.7687173534532792 [0.198171,0.996751] recall 0.7742309924550204 [0.21748,0.996751] relative_absolute_error 0.5723610244622033 relative_absolute_error 0.5579707888423083 relative_absolute_error 0.5785837224428522 relative_absolute_error 0.559392370469932 relative_absolute_error 0.563657115352803 relative_absolute_error 0.5682103768384978 relative_absolute_error 0.5554096424416355 relative_absolute_error 0.5603877058181932 relative_absolute_error 0.5667880730166243 relative_absolute_error 0.5532761867088253 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 0.4832560485613546 root_mean_squared_error 0.4772151104866865 root_mean_squared_error 0.4859499712198526 root_mean_squared_error 0.47782264110886563 root_mean_squared_error 0.4796406158724947 root_mean_squared_error 0.4815215775015027 root_mean_squared_error 0.4760667832607122 root_mean_squared_error 0.47819548597728734 root_mean_squared_error 0.4809185446067981 root_mean_squared_error 0.47515156270918407 root_relative_squared_error 1.0700123640689352 root_relative_squared_error 1.0563146794536902 root_relative_squared_error 1.0756492759756173 root_relative_squared_error 1.0576594472540455 root_relative_squared_error 1.061683530707185 root_relative_squared_error 1.066079137664024 root_relative_squared_error 1.0540023323616936 root_relative_squared_error 1.058715237582275 root_relative_squared_error 1.064744034901432 root_relative_squared_error 1.0519760523735606 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 1247728.0290679918 usercpu_time_millis 1051665.9385640013 usercpu_time_millis 1122809.4093330002 usercpu_time_millis 1255337.6326439974 usercpu_time_millis 1182451.1041370062 usercpu_time_millis 1265492.0630810012 usercpu_time_millis 1182448.5926310008 usercpu_time_millis 1266058.9464639998 usercpu_time_millis 1192089.9031650042 usercpu_time_millis 1222616.5180679965 usercpu_time_millis_testing 36823.812386995996 usercpu_time_millis_testing 26544.21197099873 usercpu_time_millis_testing 26720.07379199931 usercpu_time_millis_testing 36196.58399900072 usercpu_time_millis_testing 26842.60307600198 usercpu_time_millis_testing 37745.07504900248 usercpu_time_millis_testing 37248.55174800177 usercpu_time_millis_testing 31175.57910799951 usercpu_time_millis_testing 37237.38213899924 usercpu_time_millis_testing 37184.648729999026 usercpu_time_millis_training 1210904.216680996 usercpu_time_millis_training 1025121.7265930027 usercpu_time_millis_training 1096089.3355410008 usercpu_time_millis_training 1219141.0486449967 usercpu_time_millis_training 1155608.5010610041 usercpu_time_millis_training 1227746.9880319987 usercpu_time_millis_training 1145200.040882999 usercpu_time_millis_training 1234883.3673560002 usercpu_time_millis_training 1154852.521026005 usercpu_time_millis_training 1185431.8693379974 wall_clock_time_millis 1247755.2781105042 wall_clock_time_millis 1051668.3702468872 wall_clock_time_millis 1122810.742855072 wall_clock_time_millis 1255368.5462474823 wall_clock_time_millis 1182464.1633033752 wall_clock_time_millis 1265503.7789344788 wall_clock_time_millis 1182467.0951366425 wall_clock_time_millis 1266070.7216262817 wall_clock_time_millis 1192099.769115448 wall_clock_time_millis 1222623.319864273 wall_clock_time_millis_testing 36824.03111457825 wall_clock_time_millis_testing 26544.14963722229 wall_clock_time_millis_testing 26720.16954421997 wall_clock_time_millis_testing 36196.863412857056 wall_clock_time_millis_testing 26842.634916305542 wall_clock_time_millis_testing 37745.06330490112 wall_clock_time_millis_testing 37248.73900413513 wall_clock_time_millis_testing 31175.75979232788 wall_clock_time_millis_testing 37237.41579055786 wall_clock_time_millis_testing 37185.940980911255 wall_clock_time_millis_training 1210931.246995926 wall_clock_time_millis_training 1025124.2206096649 wall_clock_time_millis_training 1096090.573310852 wall_clock_time_millis_training 1219171.6828346252 wall_clock_time_millis_training 1155621.5283870697 wall_clock_time_millis_training 1227758.7156295776 wall_clock_time_millis_training 1145218.3561325073 wall_clock_time_millis_training 1234894.9618339539 wall_clock_time_millis_training 1154862.3533248901 wall_clock_time_millis_training 1185437.3788833618