10132499 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) 8057971 axis 0 8778 copy true 8778 missing_values "NaN" 8778 strategy "mean" 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 13 8783 min_samples_split 20 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 16258 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 21185536 description https://api.openml.org/data/download/21185536/description.xml -1 21185537 predictions https://api.openml.org/data/download/21185537/predictions.arff area_under_roc_curve 0.8465425741792931 [0.829447,0.869042,0.904257,0.892677,0.899384,0.856001,0.828677,0.896642,0.833314,0.804411,0.926156,0.929352,0.832722,0.825245,0.835701,0.818497,0.903823,0.815795,0.793383,0.75578,0.863498,0.833195,0.865017,0.999744,0.80015,0.817077,0.821911,0.834103,0.864958,0.870245,0.953086,0.902383,0.832919,0.781704,0.930496,0.870028,0.793383,0.751677,0.958077,0.9651,0.765132,0.860184,0.839173,0.861072,0.835642,0.75947,0.932232,0.804293,0.720151,0.832406,0.953697,0.820983,0.924045,0.812993,0.795553,0.684876,0.966955,0.926215,0.889106,0.821753,0.77326,0.79218,0.926748,0.636955,0.837634,0.925406,0.81757,0.933436,0.658953,0.719322,0.745778,0.931818,0.835385,0.864051,0.702869,0.929036,0.901121,0.744949,0.896544,0.9273,0.896327,0.763356,0.761719,0.822798,0.857323,0.866951,0.884411,0.849057,0.73108,0.769137,0.965653,0.96151,0.887468,0.785077,0.895537,0.862334,0.891256,0.893782,0.791963,0.852214] average_cost 0 f_measure 0.36391506935362117 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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] precision 0.3785108755242095 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[1,0.993711,1,0.474684,1,0.716772,0.984177,0.996835,0.493711,0.996835,0.984277,1,0.732595,0.484277,0.477987,0.458861,1,0.462264,0.971519,0.477848,0.977848,0.735759,0.727848,1,0.987342,0.977987,0.427673,0.735759,1,0.738924,0.981013,0.996855,1,0.984277,0.981013,1,0.735759,0.987342,0.984177,0.996855,0.72943,0.727848,0.993671,0.484177,1,0.732595,0.996835,1,0.924528,0.484277,0.974843,0.71519,0.735759,0.993711,0.735759,0.710443,1,0.992089,0.726266,0.990566,0.490506,0.732595,0.987421,0.727848,0.743671,0.984277,0.974843,0.743671,0.46519,0.724684,0.455975,1,0.996835,0.474843,0.718354,1,0.987421,0.908805,1,0.955975,0.971519,1,0.719937,0.996835,0.46519,0.737342,0.990506,0.481132,1,0.727848,1,0.993671,0.987342,0.968553,0.462264,0.993671,0.987342,0.990566,0.693038,0.973101] area_under_roc_curve 0.869348827720723 [0.471698,0.996835,1,1,0.996835,0.990566,0.724684,0.974843,0.974684,1,0.481132,0.996855,0.996835,0.743671,0.996835,0.981132,0.993711,0.474843,1,1,0.993671,1,0.993711,1,0.745253,0.981132,0.474843,0.990506,0.72943,1,0.990506,0.996835,0.943396,0.962264,0.996835,1,0.735759,0.718354,0.993671,0.996855,0.484277,0.726266,0.496835,0.735759,0.487342,0.490506,0.990566,0.97943,0.455975,0.996855,0.987342,0.984177,0.990506,0.700949,0.734177,0.465409,0.75,0.962264,0.971519,0.973101,0.745253,0.990506,0.981013,0.723101,0.996855,1,0.985759,0.5,0.72943,0.987421,0.987421,0.981132,0.743671,0.993671,0.436709,0.732595,0.996855,0.996855,1,0.993671,0.996835,0.987421,0.468553,0.981132,0.981013,0.996835,0.946203,0.941456,0.459119,0.987342,1,0.746835,0.996835,0.707278,0.993711,0.724684,0.955975,0.992089,0.96519,0.996855] area_under_roc_curve 0.8573420458164154 [0.987421,0.481013,0.496855,0.971519,0.993671,0.987421,0.708861,0.481132,0.735759,0.987421,0.996855,0.993711,0.996835,0.987342,0.740506,1,1,0.981132,0.993671,0.996855,1,0.996835,1,1,0.742089,0.45283,0.996855,0.743671,0.484177,0.996855,0.738924,0.75,1,0.959119,0.987342,0.732595,0.727848,0.985759,0.984177,1,0.974843,0.969937,0.746835,0.738924,1,0.738924,0.993711,0.732595,0.984277,0.474843,0.990506,0.704114,0.993671,0.966772,0.737342,0.462264,1,0.987421,0.468354,1,0.996835,0.724684,0.992089,0.719937,0.990566,0.5,0.933544,0.993711,0.985759,0.45283,0.455975,0.493711,0.738924,0.990506,0.726266,0.996835,1,0.468553,0.477987,0.996835,1,0.481132,0.996855,0.477987,0.973101,1,0.969937,0.938291,0.45283,0.727848,1,0.996835,0.996835,0.72943,1,0.993671,0.996855,1,0.992089,0.962264] 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 0.4251648308263256 kappa 0.3998736576121289 kappa 0.35588270118798593 kappa 0.3998499624906226 kappa 0.3682381742083235 kappa 0.3373826615129763 kappa 0.33706889748244023 kappa 0.3370427370664141 kappa 0.393939393939394 kappa 0.4129019530479385 kb_relative_information_score 88.60882141925181 kb_relative_information_score 87.71207515577218 kb_relative_information_score 84.76769061221341 kb_relative_information_score 85.83133626569423 kb_relative_information_score 77.9937189525235 kb_relative_information_score 83.81596519459168 kb_relative_information_score 81.19637991480036 kb_relative_information_score 80.63627574632049 kb_relative_information_score 90.20899451525703 kb_relative_information_score 90.24728654925337 mean_absolute_error 0.013673977122883105 mean_absolute_error 0.014114910007507933 mean_absolute_error 0.014059743330546609 mean_absolute_error 0.014210277909043178 mean_absolute_error 0.014351618979202101 mean_absolute_error 0.014329994692106812 mean_absolute_error 0.014757453420930341 mean_absolute_error 0.014438545653202444 mean_absolute_error 0.013878962157782272 mean_absolute_error 0.013653694955294851 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 0.43125 predictive_accuracy 0.40625 predictive_accuracy 0.3625 predictive_accuracy 0.40625 predictive_accuracy 0.375 predictive_accuracy 0.34375 predictive_accuracy 0.34375 predictive_accuracy 0.34375 predictive_accuracy 0.4 predictive_accuracy 0.41875 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 0.43125 [0,1,1,0,0.5,0,1,1,1,0,0.5,0.5,0,0,1,1,1,0,0,0,1,0,1,1,0,0.5,0,0.5,0.5,0,0,1,0.5,0,1,0,0,0,0,1,1,0,1,1,1,0.5,1,1,0,0.5,0.5,0.5,0,0,1,0,1,1,0,0,0,1,0,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,0.5,1,0,0.5,0,0.5,0,0,1,0.5,0.5,0,0,0,0,1,0,1,0,0,0.5,0,0.5,0,1] recall 0.40625 [1,1,0.5,1,0,0,0.5,1,0,1,0.5,0.5,0.5,0.5,1,0,0.5,0,0.5,0,0,0,0,1,1,0,0.5,0,0.5,1,0.5,1,0.5,0,0,1,0,0,0,1,1,1,1,0,0.5,0,1,0,0,1,0,0.5,0,0.5,1,0,1,0,0,0,1,1,1,0,1,1,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0,0.5,0.5,0,0,0,0,1,0,0.5,0,0,0,0.5,1,0,0,0.5,0.5,1,0,0,1] recall 0.3625 [0,0.5,0,1,0.5,0,0,0.5,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0,1,1,1,0,1,0.5,0,0,1,0,0,1,1,0,0,1,1,0,0,0.5,0,0,0.5,1,1,0,0,0,0,1,0.5,1,0,0,0,0.5,1,0.5,0.5,0,1,0,0,0,1,0,0.5,0,0,1,0,0.5,1,0,0,1,0,1,0,0,0,0,1,0.5,1,0,0.5,0.5,1,0,0.5,0,0.5] recall 0.40625 [0.5,1,1,0,0.5,0,1,0,0,0,1,0.5,0,0,1,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0,0,0.5,1,1,0.5,0.5,1,0,1,0,0,1,0,0,1,0,1,1,1,1,0,1,1,1,0,0,1,0,1,1,0,0.5,1,0,1,0,0.5,0,0.5,1,0,0.5,0,0,1,1,0,0.5,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0.5,0,0,0] recall 0.375 [0.5,1,0.5,1,0,0.5,0,0,0,0,0,0.5,1,0,1,0.5,0.5,1,1,0,0,0,1,1,1,0,0.5,1,1,0.5,0,0,0,0,1,1,0.5,0,0,1,1,0.5,0,1,1,0,1,0,0,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0.5,0.5,0,0,0.5,0,0,0,0,0,0,0.5,1,0,0,1,0.5,0,0.5,0,0,1,0,0.5,0,1,0,1,0,0,1,1,0,0,0.5,0,0.5,1,0,0] recall 0.34375 [0,1,1,0,1,0,0,0.5,1,0.5,0,1,1,0,1,0,1,0.5,0,0,1,0.5,0,1,0.5,0,0.5,1,1,0.5,0,1,0,0,0,1,0,0.5,0,1,0,0.5,1,1,1,0,1,0,0,0.5,1,0,0,0,0,0,1,1,0,0,0.5,0,0,0,0.5,0.5,0,1,0,0,0,0.5,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0.5,0,0,0.5] recall 0.34375 [0.5,0,1,0,1,0,0,1,0,0,1,1,0.5,1,0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0.5,0,1,0.5,0,1,0,1,0.5,0,0.5,0.5,0,0,0,0.5,0,0,0,0.5,0.5,0,1,1,0,0.5,0,0,0,1,0.5,0.5,0,0,0,1,0,0.5,1,0,1,0.5,0,0,1,0,0,0,1,1,0,1,0,0.5,0,0,0,1,1,0,0,0,0,1,0,0.5,0,0,0.5,0.5,0,0,0.5] recall 0.34375 [0.5,1,1,0,1,0,0.5,1,0,0.5,0,1,0,0,0,0,1,0,0,0,0,0.5,0,1,0.5,0,0,0.5,1,0,0,1,1,1,0,1,0.5,0,0,1,0,0.5,1,0,1,0,1,1,0,0,0,0,0.5,1,0,0,1,0.5,0,0,0,0.5,1,0,0.5,0,0,0.5,0,0,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0,1,1,0,0,0,1,0.5,0,0,0] recall 0.4 [0,1,1,0,0,1,0.5,0,0,1,0,1,1,0,1,0,1,0,1,0,0.5,1,1,1,0.5,0,0,1,0.5,1,0,1,0,0,1,1,0.5,0,0,1,0,0.5,0,0.5,0,0,1,0,0,1,0.5,0,1,0,0,0,0.5,0,0,0,0,0,0.5,0,1,1,0.5,0,0.5,0,0,0,0.5,0,0,0.5,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,0,1,0,0.5,0,1,0.5,0,0.5,0,1] recall 0.41875 [0,0,0,0,1,0,0,0,0.5,0,0,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,0.5,0,0,0.5,0,1,0,0.5,1,0,0.5,0.5,0,0.5,0,1,0,0,0.5,0.5,1,0,1,0.5,1,0,0.5,0,1,0,0.5,0,1,1,0,1,1,0,0.5,0,1,0,0,1,0.5,0,0,0,0,1,0.5,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0] relative_absolute_error 0.6906049051961154 relative_absolute_error 0.7128742428034297 relative_absolute_error 0.7100880469973022 relative_absolute_error 0.717690803487028 relative_absolute_error 0.7248292413738423 relative_absolute_error 0.7237371056619589 relative_absolute_error 0.7453259303500162 relative_absolute_error 0.7292194774344657 relative_absolute_error 0.7009576847364771 relative_absolute_error 0.6895805532977185 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 0.08854982508618944 root_mean_squared_error 0.08859495410441938 root_mean_squared_error 0.09062604817865297 root_mean_squared_error 0.09019031282847483 root_mean_squared_error 0.09338631254016623 root_mean_squared_error 0.09129439121505682 root_mean_squared_error 0.0927908586398614 root_mean_squared_error 0.09153850851775133 root_mean_squared_error 0.08800578759330552 root_mean_squared_error 0.08774131268148537 root_relative_squared_error 0.8899592274620914 root_relative_squared_error 0.8904127911609575 root_relative_squared_error 0.9108260546704942 root_relative_squared_error 0.9064467496267516 root_relative_squared_error 0.9385677553048256 root_relative_squared_error 0.9175431549220044 root_relative_squared_error 0.9325832184343262 root_relative_squared_error 0.9199966261276751 root_relative_squared_error 0.88449144492942 root_relative_squared_error 0.8818333720537539 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 151.92138300153601 usercpu_time_millis 138.3049080013734 usercpu_time_millis 134.4243639978231 usercpu_time_millis 105.48691199983296 usercpu_time_millis 104.97321900038514 usercpu_time_millis 103.73176699795295 usercpu_time_millis 105.47896699972625 usercpu_time_millis 105.42435999923327 usercpu_time_millis 106.45313700115366 usercpu_time_millis 106.73453300114488 usercpu_time_millis_testing 1.7379250002704794 usercpu_time_millis_testing 1.7427100010536378 usercpu_time_millis_testing 1.7342069986625575 usercpu_time_millis_testing 1.383677999911015 usercpu_time_millis_testing 1.3490729998011375 usercpu_time_millis_testing 1.5846249989408534 usercpu_time_millis_testing 1.4865390003251377 usercpu_time_millis_testing 1.4776339994568843 usercpu_time_millis_testing 1.4817060000495985 usercpu_time_millis_testing 1.517734001026838 usercpu_time_millis_training 150.18345800126554 usercpu_time_millis_training 136.56219800031977 usercpu_time_millis_training 132.69015699916054 usercpu_time_millis_training 104.10323399992194 usercpu_time_millis_training 103.624146000584 usercpu_time_millis_training 102.14714199901209 usercpu_time_millis_training 103.99242799940112 usercpu_time_millis_training 103.94672599977639 usercpu_time_millis_training 104.97143100110407 usercpu_time_millis_training 105.21679900011804