10591714 32911 tharu vk 5 Supervised Classification predictive_accuracy 18983 sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(2) 8304487 Python_3.7.3. Sklearn_0.20.0. NumPy_1.21.5. SciPy_1.7.3. n_jobs null 18952 remainder "passthrough" 18952 sparse_threshold 0.3 18952 transformer_weights null 18952 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 27, 28, 29, 30, 31, 32, 39, 40, 41, 42, 43, 44, 51, 52, 53, 54, 55, 56, 63, 64, 65, 66, 67, 68, 75, 76, 77, 78, 79, 80, 87, 88, 89, 90, 91, 92, 99, 100, 101, 102, 103, 104, 111, 112, 113, 114, 115, 116, 123, 124, 125, 126, 127, 128, 135, 136, 137, 138, 139, 140, 147, 148, 149, 150, 151, 152, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [1, 21, 22, 23, 24, 25, 26, 33, 34, 35, 36, 37, 38, 45, 46, 47, 48, 49, 50, 57, 58, 59, 60, 61, 62, 69, 70, 71, 72, 73, 74, 81, 82, 83, 84, 85, 86, 93, 94, 95, 96, 97, 98, 105, 106, 107, 108, 109, 110, 117, 118, 119, 120, 121, 122, 129, 130, 131, 132, 133, 134, 141, 142, 143, 144, 145, 146, 153, 154, 155, 156, 157, 158]}}] 18952 memory null 18953 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] 18953 axis 0 18954 copy true 18954 missing_values "NaN" 18954 strategy "mean" 18954 verbose 0 18954 copy true 18955 with_mean true 18955 with_std true 18955 memory null 18956 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"}}] 18956 copy true 18957 fill_value -1 18957 missing_values NaN 18957 strategy "constant" 18957 verbose 0 18957 categorical_features null 18958 categories null 18958 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 18958 handle_unknown "ignore" 18958 n_values null 18958 sparse true 18958 threshold 0.0 18959 class_weight null 18971 criterion "gini" 18971 max_depth null 18971 max_features 1.0 18971 max_leaf_nodes null 18971 min_impurity_decrease 0.0 18971 min_impurity_split null 18971 min_samples_leaf 5 18971 min_samples_split 16 18971 min_weight_fraction_leaf 0.0 18971 presort false 18971 random_state 0 18971 splitter "best" 18971 memory null 18983 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}] 18983 openml-python Sklearn_0.20.0. 5 arrhythmia https://www.openml.org/data/download/5/dataset_5_arrhythmia.arff -1 22111561 description https://api.openml.org/data/download/22111561/description.xml -1 22111562 predictions https://api.openml.org/data/download/22111562/predictions.arff area_under_roc_curve 0.8111094068709102 [0.837711,0.780554,0.987796,0.852174,0.696163,0.906136,0.469933,0.478889,0.885001,0.81199,0.0,0.0,0.0,0.460938,0.454139,0.576744] average_cost 0 kappa 0.5092993976312357 kb_relative_information_score 0.4990053402756713 mean_absolute_error 0.0461099180747964 mean_prior_absolute_error 0.08554099538612797 weighted_recall 0.6814159292035398 [0.840816,0.454545,1,0.6,0.384615,0.64,0,0,0.777778,0.56,0.0,0.0,0.0,0,0,0.090909] number_of_instances 452 [245,44,15,15,13,25,3,2,9,50,0,0,0,4,5,22] predictive_accuracy 0.6814159292035399 prior_entropy 2.412436334943334 relative_absolute_error 0.5390388300563774 root_mean_prior_squared_error 0.20549825255273738 root_mean_squared_error 0.17835393893190343 root_relative_squared_error 0.8679097594084512 total_cost 0 area_under_roc_curve 0.8384506918339443 [0.88381,0.817073,0.966667,0.965909,0.744318,0.727273,0.0,0.488889,0.0,0.897561,0.0,0.0,0.0,0.0,0.0,0.523256] area_under_roc_curve 0.740928199584085 [0.760952,0.736585,1,0.732955,0.455556,1,0.0,0.488889,1,0.731707,0.0,0.0,0.0,0.0,0.488889,0.409091] area_under_roc_curve 0.7436545689452667 [0.712,0.8275,1,0.453488,0.988636,0.982558,0.0,0.0,0.5,0.875,0.0,0.0,0.0,0.0,0.477273,0.656977] area_under_roc_curve 0.8184002818886539 [0.872,0.73,0.988636,1,1,0.72093,0.0,0.0,0.5,0.845,0.0,0.0,0.0,0.0,0.431818,0.395349] area_under_roc_curve 0.8794569919272132 [0.908,0.969512,1,0.732558,0.465909,0.97093,0.0,0.0,1,0.855,0.0,0.0,0.0,0.0,0.465909,0.69186] area_under_roc_curve 0.7919864269467218 [0.834325,0.667683,1,0.988636,0.988636,0.825397,0.488636,0.0,1,0.77,0.0,0.0,0.0,0.0,0.431818,0.360465] area_under_roc_curve 0.8012962976604499 [0.861111,0.783537,0.982558,0.988636,0.431818,0.821429,0.477273,0.0,1,0.63,0.0,0.0,0.0,0.443182,0.0,0.668605] area_under_roc_curve 0.7690047832674038 [0.777778,0.667683,0.965116,1,0.443182,0.968254,0.477273,0.0,0.988636,0.765,0.0,0.0,0.0,0.454545,0.0,0.622093] area_under_roc_curve 0.8538878139360274 [0.853175,0.823171,0.988372,1,0.418605,1,0.0,0.0,1,0.88,0.0,0.0,0.0,0.488636,0.0,0.976744] area_under_roc_curve 0.8874356399257138 [0.925595,0.823171,1,1,0.965116,1,0.0,0.0,0.988636,0.88,0.0,0.0,0.0,0.465909,0.0,0.547619] 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 kappa 0.5050437121721587 kappa 0.41335333833458365 kappa 0.46892210857592437 kappa 0.5190839694656488 kappa 0.5468628969790861 kappa 0.5401459854014599 kappa 0.4992581602373886 kappa 0.5168711656441718 kappa 0.5614692653673163 kappa 0.5185185185185186 kb_relative_information_score 0.4619369084675441 kb_relative_information_score 0.4214682089800309 kb_relative_information_score 0.43233794064940817 kb_relative_information_score 0.5058359940190347 kb_relative_information_score 0.566507021574301 kb_relative_information_score 0.5035326204596287 kb_relative_information_score 0.4534408651203361 kb_relative_information_score 0.4556305656467952 kb_relative_information_score 0.5974512776634817 kb_relative_information_score 0.5945821138552971 mean_absolute_error 0.05140182884748103 mean_absolute_error 0.05122644565579348 mean_absolute_error 0.05210217251883919 mean_absolute_error 0.043727954144620815 mean_absolute_error 0.038585673585673594 mean_absolute_error 0.04424011174011174 mean_absolute_error 0.0502282671032671 mean_absolute_error 0.0523063973063973 mean_absolute_error 0.039957881624548296 mean_absolute_error 0.037091149591149584 mean_prior_absolute_error 0.08534234485321444 mean_prior_absolute_error 0.08546427907840952 mean_prior_absolute_error 0.08460351377018045 mean_prior_absolute_error 0.08460351377018045 mean_prior_absolute_error 0.08477564102564104 mean_prior_absolute_error 0.08615265906932575 mean_prior_absolute_error 0.08615859449192784 mean_prior_absolute_error 0.08615859449192784 mean_prior_absolute_error 0.08609924026590694 mean_prior_absolute_error 0.08605769230769232 number_of_instances 46 [25,5,1,2,2,2,0,1,0,5,0,0,0,0,0,3] number_of_instances 46 [25,5,1,2,1,2,0,1,1,5,0,0,0,0,1,2] number_of_instances 45 [25,5,1,2,1,2,0,0,1,5,0,0,0,0,1,2] number_of_instances 45 [25,5,1,2,1,2,0,0,1,5,0,0,0,0,1,2] number_of_instances 45 [25,4,2,2,1,2,0,0,1,5,0,0,0,0,1,2] number_of_instances 45 [24,4,2,1,1,3,1,0,1,5,0,0,0,0,1,2] number_of_instances 45 [24,4,2,1,1,3,1,0,1,5,0,0,0,1,0,2] number_of_instances 45 [24,4,2,1,1,3,1,0,1,5,0,0,0,1,0,2] number_of_instances 45 [24,4,2,1,2,3,0,0,1,5,0,0,0,1,0,2] number_of_instances 45 [24,4,1,1,2,3,0,0,1,5,0,0,0,1,0,3] predictive_accuracy 0.6521739130434783 predictive_accuracy 0.6304347826086957 predictive_accuracy 0.6666666666666667 predictive_accuracy 0.6888888888888889 predictive_accuracy 0.7111111111111111 predictive_accuracy 0.6888888888888889 predictive_accuracy 0.6666666666666667 predictive_accuracy 0.6888888888888889 predictive_accuracy 0.7111111111111111 predictive_accuracy 0.7111111111111111 prior_entropy 2.3800279266643507 prior_entropy 2.432724150197092 prior_entropy 2.324886859782311 prior_entropy 2.324886859782311 prior_entropy 2.3580391508118588 prior_entropy 2.4745297016384846 prior_entropy 2.480374910657013 prior_entropy 2.480374910657013 prior_entropy 2.440211467944622 prior_entropy 2.4285767578100224 relative_absolute_error 0.602301576502148 relative_absolute_error 0.5993901336112084 relative_absolute_error 0.6158393451644468 relative_absolute_error 0.5168574234801259 relative_absolute_error 0.4551504785909322 relative_absolute_error 0.5135083724405116 relative_absolute_error 0.582974541303282 relative_absolute_error 0.6070943660913348 relative_absolute_error 0.46409098966661355 relative_absolute_error 0.43100330251168223 root_mean_prior_squared_error 0.20501434405747065 root_mean_prior_squared_error 0.2053115084317244 root_mean_prior_squared_error 0.20320445414970847 root_mean_prior_squared_error 0.20320445414970847 root_mean_prior_squared_error 0.20362754588154708 root_mean_prior_squared_error 0.20698114765704212 root_mean_prior_squared_error 0.20699548523585812 root_mean_prior_squared_error 0.20699548523585812 root_mean_prior_squared_error 0.20685206472744588 root_mean_prior_squared_error 0.20675161117580884 root_mean_squared_error 0.18357069350608918 root_mean_squared_error 0.19615446344359497 root_mean_squared_error 0.189177862885402 root_mean_squared_error 0.18053555086186351 root_mean_squared_error 0.1631649956243515 root_mean_squared_error 0.1774999014836193 root_mean_squared_error 0.18039279816216747 root_mean_squared_error 0.19479432806658817 root_mean_squared_error 0.16199593197048717 root_mean_squared_error 0.14998982724583784 root_relative_squared_error 0.8954041452564399 root_relative_squared_error 0.9553992610639526 root_relative_squared_error 0.9309730127570306 root_relative_squared_error 0.8884428819107285 root_relative_squared_error 0.8012913720389616 root_relative_squared_error 0.8575655488089581 root_relative_squared_error 0.87148180046836 root_relative_squared_error 0.9410559261455993 root_relative_squared_error 0.7831487308765207 root_relative_squared_error 0.7254590491113306 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 62.5 usercpu_time_millis 62.5 usercpu_time_millis 62.5 usercpu_time_millis 62.5 usercpu_time_millis 62.5 usercpu_time_millis 62.5 usercpu_time_millis 78.125 usercpu_time_millis 62.5 usercpu_time_millis 62.5 usercpu_time_millis 62.5 usercpu_time_millis_testing 15.625 usercpu_time_millis_testing 0 usercpu_time_millis_testing 15.625 usercpu_time_millis_testing 0 usercpu_time_millis_testing 15.625 usercpu_time_millis_testing 15.625 usercpu_time_millis_testing 0 usercpu_time_millis_testing 0 usercpu_time_millis_testing 0 usercpu_time_millis_testing 0 usercpu_time_millis_training 46.875 usercpu_time_millis_training 62.5 usercpu_time_millis_training 46.875 usercpu_time_millis_training 62.5 usercpu_time_millis_training 46.875 usercpu_time_millis_training 46.875 usercpu_time_millis_training 78.125 usercpu_time_millis_training 62.5 usercpu_time_millis_training 62.5 usercpu_time_millis_training 62.5 wall_clock_time_millis 62.50715255737305 wall_clock_time_millis 62.50596046447754 wall_clock_time_millis 62.50643730163574 wall_clock_time_millis 78.13310623168945 wall_clock_time_millis 62.50929832458496 wall_clock_time_millis 78.13334465026855 wall_clock_time_millis 78.12809944152832 wall_clock_time_millis 62.50810623168945 wall_clock_time_millis 62.53385543823242 wall_clock_time_millis 62.506675720214844 wall_clock_time_millis_testing 15.626668930053711 wall_clock_time_millis_testing 0 wall_clock_time_millis_testing 15.62809944152832 wall_clock_time_millis_testing 15.625953674316406 wall_clock_time_millis_testing 15.62952995300293 wall_clock_time_millis_testing 15.625953674316406 wall_clock_time_millis_testing 0 wall_clock_time_millis_testing 0 wall_clock_time_millis_testing 0 wall_clock_time_millis_testing 0 wall_clock_time_millis_training 46.880483627319336 wall_clock_time_millis_training 62.50596046447754 wall_clock_time_millis_training 46.87833786010742 wall_clock_time_millis_training 62.50715255737305 wall_clock_time_millis_training 46.87976837158203 wall_clock_time_millis_training 62.50739097595215 wall_clock_time_millis_training 78.12809944152832 wall_clock_time_millis_training 62.50810623168945 wall_clock_time_millis_training 62.53385543823242 wall_clock_time_millis_training 62.506675720214844 weighted_recall 0.6521739130434783 [0.76,0.6,1,1,0.5,0.5,0.0,0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0] weighted_recall 0.6304347826086957 [0.8,0.4,1,0.5,0,0.5,0.0,0,1,0.6,0.0,0.0,0.0,0.0,0,0] weighted_recall 0.6666666666666666 [0.8,0.4,1,0,1,0.5,0.0,0.0,0,0.8,0.0,0.0,0.0,0.0,0,0.5] weighted_recall 0.6888888888888889 [0.84,0.2,1,1,1,0.5,0.0,0.0,0,0.8,0.0,0.0,0.0,0.0,0,0] weighted_recall 0.7111111111111111 [0.88,0.75,1,0.5,0,0.5,0.0,0.0,1,0.4,0.0,0.0,0.0,0.0,0,0] weighted_recall 0.6888888888888889 [0.833333,0.25,1,1,1,0.666667,0,0.0,1,0.6,0.0,0.0,0.0,0.0,0,0] weighted_recall 0.6666666666666666 [0.833333,0.5,1,1,0,0.666667,0,0.0,1,0.2,0.0,0.0,0.0,0,0.0,0.5] weighted_recall 0.6888888888888889 [0.875,0.5,1,1,0,0.666667,0,0.0,1,0.4,0.0,0.0,0.0,0,0.0,0] weighted_recall 0.7111111111111111 [0.833333,0.5,1,0,0,1,0.0,0.0,1,0.8,0.0,0.0,0.0,0,0.0,0] weighted_recall 0.7111111111111111 [0.958333,0.5,1,0,0.5,0.666667,0.0,0.0,1,0.4,0.0,0.0,0.0,0,0.0,0]