10553016 8323 Heinrich Peters 219 Supervised Classification 18607 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)),logisticregression=sklearn.linear_model.logistic.LogisticRegression)(2) 8275719 copy true 17405 with_mean true 17405 with_std true 17405 add_indicator false 17407 copy true 17407 fill_value null 17407 missing_values NaN 17407 strategy "most_frequent" 17407 verbose 0 17407 categorical_features null 17408 categories null 17408 drop null 17408 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 17408 handle_unknown "ignore" 17408 n_values null 17408 sparse true 17408 C 1.0 17462 class_weight null 17462 dual false 17462 fit_intercept true 17462 intercept_scaling 1 17462 l1_ratio null 17462 max_iter 100 17462 multi_class "warn" 17462 n_jobs null 17462 penalty "l2" 17462 random_state 1 17462 solver "liblinear" 17462 tol 0.0001 17462 verbose 0 17462 warm_start false 17462 n_jobs null 18299 remainder "drop" 18299 sparse_threshold 0.3 18299 transformer_weights null 18299 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "num", "step_name": "num", "argument_1": [true, false, true, true, true, true, true, true]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [false, true, false, false, false, false, false, false]}}] 18299 verbose false 18299 memory null 18300 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] 18300 verbose false 18300 memory null 18301 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}] 18301 verbose false 18301 memory null 18607 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": "logisticregression", "step_name": "logisticregression"}}] 18607 verbose false 18607 openml-python Sklearn_0.21.2. 151 electricity https://www.openml.org/data/download/2419/electricity-normalized.arff -1 22030987 description https://api.openml.org/data/download/22030987/description.xml -1 22030988 predictions https://api.openml.org/data/download/22030988/predictions.arff area_under_roc_curve 0.8273440678470415 [0.827344,0.827344] average_cost 0 f_measure 0.7564233266357969 [0.689128,0.806071] kappa 0.4986535101240343 kb_relative_information_score 0.35337887452152894 mean_absolute_error 0.3293994422436843 mean_prior_absolute_error 0.4886137014923867 weighted_recall 0.7611449505649718 [0.62359,0.862627] number_of_instances 45312 [19237,26075] precision 0.7622420587730101 [0.77006,0.756474] predictive_accuracy 0.7611449505649718 prior_entropy 0.9835093906388539 relative_absolute_error 0.6741510547853861 root_mean_prior_squared_error 0.4942738102212943 root_mean_squared_error 0.4055035153279296 root_relative_squared_error 0.8204025925354596 total_cost 0 unweighted_recall 0.7431084971230587 [0.62359,0.862627] area_under_roc_curve 0.8283551809241994 [0.828355,0.828355] area_under_roc_curve 0.8234603985179139 [0.82346,0.82346] area_under_roc_curve 0.8143447953574536 [0.814345,0.814345] area_under_roc_curve 0.8284879107663918 [0.828488,0.828488] area_under_roc_curve 0.8285214044707716 [0.828521,0.828521] area_under_roc_curve 0.8218481826076762 [0.821848,0.821848] area_under_roc_curve 0.8414300376325694 [0.84143,0.84143] area_under_roc_curve 0.8349579995469757 [0.834958,0.834958] area_under_roc_curve 0.8206654033032487 [0.820665,0.820665] area_under_roc_curve 0.8319654872084455 [0.831965,0.831965] 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 0.7587117755354916 [0.691777,0.808092] f_measure 0.7587515070056734 [0.692814,0.807395] f_measure 0.7472544379293273 [0.676657,0.799356] f_measure 0.7558283698835256 [0.691085,0.80361] f_measure 0.7510600510979292 [0.679826,0.803632] f_measure 0.7557997887831432 [0.686723,0.80678] f_measure 0.7701710596265561 [0.710042,0.814547] f_measure 0.7625622330174016 [0.696078,0.811584] f_measure 0.7493831391996889 [0.678798,0.801429] f_measure 0.7545362375984936 [0.686944,0.804375] kappa 0.5033680538234383 kappa 0.5034255905687096 kappa 0.4798434797876147 kappa 0.4974320726168148 kappa 0.487756914963347 kappa 0.49746725008891746 kappa 0.5269611590272805 kappa 0.5112619043379285 kappa 0.4841504247630468 kappa 0.4947072410609212 kb_relative_information_score 0.3523206487642655 kb_relative_information_score 0.34983660075492584 kb_relative_information_score 0.335536652824943 kb_relative_information_score 0.3561309219099662 kb_relative_information_score 0.35527986495587155 kb_relative_information_score 0.3494489746339163 kb_relative_information_score 0.36854258209968854 kb_relative_information_score 0.3630183149859962 kb_relative_information_score 0.3446031266817122 kb_relative_information_score 0.3590725262183557 mean_absolute_error 0.3302133852265749 mean_absolute_error 0.3311892498188058 mean_absolute_error 0.3371924951297387 mean_absolute_error 0.3279686302393491 mean_absolute_error 0.3281585886356631 mean_absolute_error 0.33137180325724014 mean_absolute_error 0.3230971006054141 mean_absolute_error 0.3251973295273277 mean_absolute_error 0.3331336119946987 mean_absolute_error 0.3264716533494932 mean_prior_absolute_error 0.48861238171727794 mean_prior_absolute_error 0.48861238171727794 mean_prior_absolute_error 0.48862652068817236 mean_prior_absolute_error 0.48862652068817236 mean_prior_absolute_error 0.48862652068817236 mean_prior_absolute_error 0.48862652068817236 mean_prior_absolute_error 0.48862652068817236 mean_prior_absolute_error 0.4885932162099533 mean_prior_absolute_error 0.4885932162099533 mean_prior_absolute_error 0.4885932162099533 number_of_instances 4532 [1924,2608] number_of_instances 4532 [1924,2608] number_of_instances 4531 [1924,2607] number_of_instances 4531 [1924,2607] number_of_instances 4531 [1924,2607] number_of_instances 4531 [1924,2607] number_of_instances 4531 [1924,2607] number_of_instances 4531 [1923,2608] number_of_instances 4531 [1923,2608] number_of_instances 4531 [1923,2608] precision 0.7647857075537084 [0.774131,0.757891] precision 0.7641920034365911 [0.771192,0.759028] precision 0.7532597997150713 [0.759379,0.748744] precision 0.7600654229790151 [0.761577,0.75895] precision 0.7583495698050214 [0.769888,0.749834] precision 0.7627169635604316 [0.774299,0.754169] precision 0.7743421594210912 [0.779019,0.77089] precision 0.769133488475952 [0.78123,0.760214] precision 0.7557222220071133 [0.763483,0.75] precision 0.7600789168562951 [0.766325,0.755473] predictive_accuracy 0.7634598411297441 predictive_accuracy 0.7632391879964696 predictive_accuracy 0.7523725446921209 predictive_accuracy 0.7598764069741779 predictive_accuracy 0.7565658794967999 predictive_accuracy 0.760979916133304 predictive_accuracy 0.7737806223791658 predictive_accuracy 0.7673802692562348 predictive_accuracy 0.754579563010373 predictive_accuracy 0.7592143014787023 prior_entropy 0.9835055532661361 prior_entropy 0.9835055532661361 prior_entropy 0.98354666368022 prior_entropy 0.98354666368022 prior_entropy 0.98354666368022 prior_entropy 0.98354666368022 prior_entropy 0.98354666368022 prior_entropy 0.9834498277152368 prior_entropy 0.9834498277152368 prior_entropy 0.9834498277152368 relative_absolute_error 0.6758187012494574 relative_absolute_error 0.6778159175066492 relative_absolute_error 0.6900822629415266 relative_absolute_error 0.6712051359337685 relative_absolute_error 0.6715938958317096 relative_absolute_error 0.6781699093830241 relative_absolute_error 0.6612352930626243 relative_absolute_error 0.6655788879958319 relative_absolute_error 0.681822016643694 relative_absolute_error 0.6681870368196131 root_mean_prior_squared_error 0.49427247515480305 root_mean_prior_squared_error 0.49427247515480305 root_mean_prior_squared_error 0.4942867777581652 root_mean_prior_squared_error 0.4942867777581652 root_mean_prior_squared_error 0.4942867777581652 root_mean_prior_squared_error 0.4942867777581652 root_mean_prior_squared_error 0.4942867777581652 root_mean_prior_squared_error 0.494253087181386 root_mean_prior_squared_error 0.494253087181386 root_mean_prior_squared_error 0.494253087181386 root_mean_squared_error 0.40453768964585907 root_mean_squared_error 0.4075663150700419 root_mean_squared_error 0.4138279162814279 root_mean_squared_error 0.40505361750753355 root_mean_squared_error 0.4051835683390439 root_mean_squared_error 0.40784911893673675 root_mean_squared_error 0.39619665477765104 root_mean_squared_error 0.40138594972221614 root_mean_squared_error 0.40962169905262424 root_mean_squared_error 0.40356010329410025 root_relative_squared_error 0.8184507735721281 root_relative_squared_error 0.824578214561502 root_relative_squared_error 0.8372223067716721 root_relative_squared_error 0.8194708734566031 root_relative_squared_error 0.8197337791974765 root_relative_squared_error 0.8251265000179329 root_relative_squared_error 0.8015522012840373 root_relative_squared_error 0.812106105419047 root_relative_squared_error 0.8287691259322315 root_relative_squared_error 0.8165049723725807 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 0.7453369928446616 [0.62526,0.865414] unweighted_recall 0.7456223773325 [0.628898,0.862347] unweighted_recall 0.7337471799497115 [0.610187,0.857307] unweighted_recall 0.7431956941450613 [0.632536,0.853855] unweighted_recall 0.7371869634527862 [0.608628,0.865746] unweighted_recall 0.7421121329349177 [0.616944,0.86728] unweighted_recall 0.7578657372961171 [0.652287,0.863445] unweighted_recall 0.7490319398051997 [0.627665,0.870399] unweighted_recall 0.7357269444151999 [0.611024,0.860429] unweighted_recall 0.7412554554329411 [0.622465,0.860046] usercpu_time_millis 225.57200000028388 usercpu_time_millis 369.5740000002843 usercpu_time_millis 376.9819999997708 usercpu_time_millis 390.48799999909534 usercpu_time_millis 379.91799999963405 usercpu_time_millis 416.77399999935005 usercpu_time_millis 385.2100000003702 usercpu_time_millis 389.9860000001354 usercpu_time_millis 377.74199999967095 usercpu_time_millis 372.3760000002585 usercpu_time_millis_testing 4.130000000259315 usercpu_time_millis_testing 7.792000000335975 usercpu_time_millis_testing 8.15599999987171 usercpu_time_millis_testing 8.73799999953917 usercpu_time_millis_testing 8.109999999760475 usercpu_time_millis_testing 8.149999999659485 usercpu_time_millis_testing 8.192000000235566 usercpu_time_millis_testing 7.95399999969959 usercpu_time_millis_testing 8.219999999710126 usercpu_time_millis_testing 7.959999999911815 usercpu_time_millis_training 221.44200000002456 usercpu_time_millis_training 361.7819999999483 usercpu_time_millis_training 368.82599999989907 usercpu_time_millis_training 381.74999999955617 usercpu_time_millis_training 371.8079999998736 usercpu_time_millis_training 408.62399999969057 usercpu_time_millis_training 377.01800000013463 usercpu_time_millis_training 382.0320000004358 usercpu_time_millis_training 369.5219999999608 usercpu_time_millis_training 364.4160000003467 wall_clock_time_millis 120.62811851501465 wall_clock_time_millis 95.18027305603027 wall_clock_time_millis 96.9548225402832 wall_clock_time_millis 100.66390037536621 wall_clock_time_millis 97.86701202392578 wall_clock_time_millis 109.88306999206543 wall_clock_time_millis 99.11584854125977 wall_clock_time_millis 100.45003890991211 wall_clock_time_millis 97.40829467773438 wall_clock_time_millis 95.71099281311035 wall_clock_time_millis_testing 2.1691322326660156 wall_clock_time_millis_testing 1.989126205444336 wall_clock_time_millis_testing 2.138853073120117 wall_clock_time_millis_testing 2.330780029296875 wall_clock_time_millis_testing 2.1240711212158203 wall_clock_time_millis_testing 2.1462440490722656 wall_clock_time_millis_testing 2.1889209747314453 wall_clock_time_millis_testing 2.086162567138672 wall_clock_time_millis_testing 2.170085906982422 wall_clock_time_millis_testing 2.0928382873535156 wall_clock_time_millis_training 118.45898628234863 wall_clock_time_millis_training 93.19114685058594 wall_clock_time_millis_training 94.81596946716309 wall_clock_time_millis_training 98.33312034606934 wall_clock_time_millis_training 95.74294090270996 wall_clock_time_millis_training 107.73682594299316 wall_clock_time_millis_training 96.92692756652832 wall_clock_time_millis_training 98.36387634277344 wall_clock_time_millis_training 95.23820877075195 wall_clock_time_millis_training 93.61815452575684 weighted_recall 0.7634598411297441 [0.62526,0.865414] weighted_recall 0.7632391879964695 [0.628898,0.862347] weighted_recall 0.7523725446921209 [0.610187,0.857307] weighted_recall 0.7598764069741779 [0.632536,0.853855] weighted_recall 0.7565658794967999 [0.608628,0.865746] weighted_recall 0.760979916133304 [0.616944,0.86728] weighted_recall 0.7737806223791658 [0.652287,0.863445] weighted_recall 0.7673802692562348 [0.627665,0.870399] weighted_recall 0.754579563010373 [0.611024,0.860429] weighted_recall 0.7592143014787023 [0.622465,0.860046]