10097845 1 Jan van Rijn 9954 Supervised Classification 8815 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)(1) 8022993 axis 0 8778 copy true 8778 missing_values "NaN" 8778 strategy "most_frequent" 8778 verbose 0 8778 copy true 8779 with_mean true 8779 with_std true 8779 memory null 8780 copy true 8781 fill_value -1 8781 missing_values NaN 8781 strategy "constant" 8781 verbose 0 8781 categorical_features null 8782 categories null 8782 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 8782 handle_unknown "ignore" 8782 n_values null 8782 sparse true 8782 class_weight null 8783 criterion "entropy" 8783 max_depth null 8783 max_features 1.0 8783 max_leaf_nodes null 8783 min_impurity_decrease 0.0 8783 min_impurity_split null 8783 min_samples_leaf 17 8783 min_samples_split 4 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 3693 8783 splitter "best" 8783 n_jobs null 8812 remainder "passthrough" 8812 sparse_threshold 0.3 8812 transformer_weights null 8812 memory null 8813 memory null 8815 threshold 0.0 8816 openml-python Sklearn_0.20.0. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 21116227 description https://api.openml.org/data/download/21116227/description.xml -1 21116228 predictions https://api.openml.org/data/download/21116228/predictions.arff area_under_roc_curve 0.8608749605429291 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0.015111129837254211 mean_prior_absolute_error 0.019800000000000036 number_of_instances 1600 [16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16] predictive_accuracy 0.333125 prior_entropy 6.6438561897747395 recall 0.333125 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[0.933962,0.477848,0.496855,0.960443,0.993671,0.990566,0.704114,0.490566,0.734177,0.981132,1,0.993711,0.990506,0.987342,1,0.993711,0.993711,1,0.993671,0.993711,0.984177,0.990506,0.996855,0.990506,1,0.440252,0.993711,0.484177,0.732595,0.990566,0.982595,0.75,0.996855,0.95283,0.971519,0.731013,0.71519,0.993671,0.984177,1,0.974843,0.974684,0.990506,0.740506,1,0.713608,0.993711,0.721519,0.990566,0.474843,0.990506,0.704114,0.993671,0.71519,0.734177,0.449686,1,0.987421,0.471519,1,0.987342,0.471519,0.484177,0.721519,0.990566,0.490566,0.958861,0.993711,0.981013,0.437107,0.455975,1,0.981013,0.990506,0.712025,0.996835,1,1,0.468553,0.996835,0.993671,0.477987,0.987421,0.474843,0.973101,0.996835,0.947785,0.935127,0.437107,0.988924,1,0.996835,0.996835,0.982595,0.990566,0.993671,0.996855,0.996835,0.716772,0.977987] 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 0.36197094125078966 kappa 0.3496191641343384 kappa 0.3305967792863909 kappa 0.32420163423202936 kappa 0.31785883467550924 kappa 0.3058020747051631 kappa 0.3117057384166074 kappa 0.31815491457207123 kappa 0.2804166009152596 kappa 0.36244920503412636 kb_relative_information_score 84.95043274396714 kb_relative_information_score 87.30799872894914 kb_relative_information_score 83.51548832154285 kb_relative_information_score 83.26375409605336 kb_relative_information_score 80.34437576277587 kb_relative_information_score 81.557880274159 kb_relative_information_score 82.48919173632584 kb_relative_information_score 80.43100713520955 kb_relative_information_score 87.66290465195704 kb_relative_information_score 88.07158665141574 mean_absolute_error 0.014887765381633342 mean_absolute_error 0.014938370302226181 mean_absolute_error 0.01497332445203348 mean_absolute_error 0.015205061197069155 mean_absolute_error 0.015218032402249717 mean_absolute_error 0.015370494889638573 mean_absolute_error 0.015362521217758256 mean_absolute_error 0.015312235141718308 mean_absolute_error 0.015150294664730956 mean_absolute_error 0.014693198723484518 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 number_of_instances 160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1] number_of_instances 160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1] number_of_instances 160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2] number_of_instances 160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2] number_of_instances 160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2] number_of_instances 160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2] number_of_instances 160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2] number_of_instances 160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2] number_of_instances 160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1] number_of_instances 160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1] predictive_accuracy 0.36875 predictive_accuracy 0.35625 predictive_accuracy 0.3375 predictive_accuracy 0.33125 predictive_accuracy 0.325 predictive_accuracy 0.3125 predictive_accuracy 0.31875 predictive_accuracy 0.325 predictive_accuracy 0.2875 predictive_accuracy 0.36875 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 recall 0.36875 [0,1,0.5,0,0.5,0,1,1,1,0,1,0.5,0,0,1,1,1,0,0,0,1,0,0,1,0,0.5,0,0,0.5,0,0,0,0.5,0,1,0,0,0,0,1,1,0,1,0,1,0.5,1,0,0,0,0.5,1,0,0,1,0,1,1,0,0,0,1,0,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,0.5,1,0,0.5,0,0,0,0,1,0,0.5,0,0.5,0,0,1,1,1,0,0,0,0,0,0,0] recall 0.35625 [1,1,0.5,1,0,0,0.5,1,0,1,0,1,0,0.5,0,0,1,0,0.5,0,0,0,0,1,0,0,0.5,0,0.5,1,0.5,1,0,0,0,1,0,0,0,1,0,1,1,0,0.5,0,1,0,0,1,0,0.5,0,0,1,0,1,0,0,0,1,1,1,0,1,0,0.5,0,0,0,0,0,0,1,1,1,0,0,0.5,0.5,0,0,0,0,1,0.5,0.5,0,0,0,0.5,1,0,0,0.5,0.5,0,0,0,1] recall 0.3375 [0,0.5,0,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,1,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0.5,0,0.5,0,0,0.5,1,1,0,0,0,0,1,0.5,1,0,0,0,1,1,0.5,0,0,1,0,0,0,0,0,0.5,0,0,1,0,0,1,0,0.5,1,0,1,0,0,0,0,1,0.5,1,0,0,0.5,1,0,0.5,0,0.5] recall 0.33125 [1,0,1,0,0.5,0,0,0,0,0,1,0.5,0,0,1,0,0.5,0,0,0,0,0,0,1,0,0,0.5,0,0,0.5,1,1,0.5,0.5,1,0,1,1,0,1,0,0,1,0,1,1,1,0,0,1,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0.5,0,0.5,1,0,0,0,0.5,0,1,0,0.5,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0.5,0,0,0] recall 0.325 [0,1,1,1,0,0,0,0.5,0,1,0,0.5,1,0,1,0.5,0.5,1,1,0,0,0,1,1,0,0,0.5,0,1,0.5,0,0,0,0,0,1,0,0,0,1,1,0.5,0,0,1,0,0,0,0,0.5,0,0,0,0,0,0,1,0,1,0,0,1,1,1,0.5,0,0,0,0,0,0,0,0,0,0,1,0.5,0,0.5,0,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0.5,0,0.5,1,0,0] recall 0.3125 [0,1,1,0,1,0,0,0.5,1,0.5,0,1,1,0,1,0,1,0,0,0,0,0.5,0,1,0,0,0.5,1,0,1,0,1,0,0,0,1,0,0.5,0,1,0,0,1,0,1,0,1,0.5,0,0.5,1,0,0,0,0,0,0,1,0,1,0.5,0,0,0,0.5,0,1,1,0,0,0,1,1,1,0,0,0.5,0,0.5,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.5] recall 0.31875 [0.5,0,1,0,1,0,0,1,0,0,1,1,0.5,0,0,0,0,0,0,0,0.5,0.5,0,1,0.5,0,0,0.5,0,1,0.5,0,1,0,1,0.5,0,0.5,0,0,0,0,0.5,0,0,0,0.5,0,0,1,1,0,0.5,0,0,0,1,0.5,0.5,0,0,0,1,0,0.5,1,0,1,0,0,0,1,0,0,0,1,1,0,1,0,0,0,0,0,1,1,0,0,0,0,1,1,0.5,0,0,0.5,0.5,0,0,0] recall 0.325 [0,1,1,0,1,0,0.5,1,0,0.5,0,1,0,0,0,0.5,1,0,0,0,0,0,0,1,0,0,0,0.5,1,0,0,1,1,1,0,1,0.5,0,0,1,0,0.5,1,0,1,0,1,1,0,0,0,0,0.5,0,0,0,0,0.5,0,0,0,0.5,1,0,1,0,0,0.5,0,0,1,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0,1,1,0,0,0,1,0,1,0,0] recall 0.2875 [0,1,0,0,0,1,0.5,0,0,0,0,1,0,0,1,0,1,0,0,0,0.5,0,1,1,0.5,0,0,0.5,0.5,1,0,0,0,0,0.5,1,0.5,0,0,1,0,0,0.5,0,0,0,1,0,0,1,0.5,0,1,0,0,0,1,0,0,0,0.5,0,1,0,1,0,0,0,0,0,0,1,0.5,0,0,0.5,1,0,1,0,0,0,0,0,0.5,1,0,0,0,0,1,0.5,0.5,0,0,0.5,0,0,0,0] recall 0.36875 [0,0,0,0,1,1,0,0,0,0,1,1,1,0,1,0,1,0,1,1,0,0,0,1,0,0,0,0,0.5,1,0,0.5,1,0,0.5,0.5,0,1,0,1,0,0,0.5,0,1,0,1,0.5,1,0,0.5,0,1,0,0.5,0,1,1,0,1,0,0,0,0,1,0,0.5,1,0.5,0,0,1,0,1,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,1,0,0,0,1,0,0,0] relative_absolute_error 0.7519073425067332 relative_absolute_error 0.7544631465770786 relative_absolute_error 0.7562285076784573 relative_absolute_error 0.76793238369036 relative_absolute_error 0.7685874950631156 relative_absolute_error 0.7762876206888155 relative_absolute_error 0.7758849099877894 relative_absolute_error 0.773345209177691 relative_absolute_error 0.765166397208633 relative_absolute_error 0.742080743610328 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_squared_error 0.0900739645071972 root_mean_squared_error 0.08942413568645338 root_mean_squared_error 0.0910700658755826 root_mean_squared_error 0.09076961101982445 root_mean_squared_error 0.09312120799595706 root_mean_squared_error 0.09157752595993644 root_mean_squared_error 0.09237625371835217 root_mean_squared_error 0.09193125543649958 root_mean_squared_error 0.0907395110071181 root_mean_squared_error 0.088821745374023 root_relative_squared_error 0.9052774050004921 root_relative_squared_error 0.8987463796175641 root_relative_squared_error 0.91528860043108 root_relative_squared_error 0.9122689155129281 root_relative_squared_error 0.935903354385553 root_relative_squared_error 0.9203887661761825 root_relative_squared_error 0.9284162821892329 root_relative_squared_error 0.9239438811793603 root_relative_squared_error 0.9119663990028316 root_relative_squared_error 0.8926921291821804 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 197.12713900025847 usercpu_time_millis 184.23334099952626 usercpu_time_millis 170.16399899966927 usercpu_time_millis 138.71835300051316 usercpu_time_millis 142.22135900035937 usercpu_time_millis 139.4073239998761 usercpu_time_millis 139.76541600004566 usercpu_time_millis 141.5304379997906 usercpu_time_millis 141.6989139997895 usercpu_time_millis 141.66636500067398 usercpu_time_millis_testing 1.719876000606746 usercpu_time_millis_testing 1.724828999613237 usercpu_time_millis_testing 1.330471000073885 usercpu_time_millis_testing 1.3431530005618697 usercpu_time_millis_testing 1.4969519997976022 usercpu_time_millis_testing 1.3409459998001694 usercpu_time_millis_testing 1.3628110000354354 usercpu_time_millis_testing 1.3823700001012185 usercpu_time_millis_testing 1.335656000264862 usercpu_time_millis_testing 1.3308640000104788 usercpu_time_millis_training 195.40726299965172 usercpu_time_millis_training 182.50851199991303 usercpu_time_millis_training 168.8335279995954 usercpu_time_millis_training 137.3751999999513 usercpu_time_millis_training 140.72440700056177 usercpu_time_millis_training 138.06637800007593 usercpu_time_millis_training 138.40260500001023 usercpu_time_millis_training 140.14806799968937 usercpu_time_millis_training 140.36325799952465 usercpu_time_millis_training 140.3355010006635