5979142 1 Jan van Rijn 9956 Supervised Classification 6969 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)(1) 4013875 bootstrap true 6902 class_weight null 6902 criterion "gini" 6902 max_depth null 6902 max_features 0.7734569738157152 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 7 6902 min_samples_split 16 6902 min_weight_fraction_leaf 0.0 6902 n_estimators 100 6902 n_jobs 1 6902 oob_score false 6902 random_state 47891 6902 verbose 0 6902 warm_start false 6902 axis 0 6947 categorical_features [] 6947 copy true 6947 fill_empty 0 6947 missing_values "NaN" 6947 strategy "most_frequent" 6947 strategy_nominal "most_frequent" 6947 verbose 0 6947 categorical_features [] 6948 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 6948 handle_unknown "ignore" 6948 n_values "auto" 6948 sparse false 6948 threshold 0.0 6949 openml-pimp openml-python Sklearn_0.18.1. study_71 1493 one-hundred-plants-texture https://www.openml.org/data/download/1592285/phpoOxxNn -1 12881621 description https://api.openml.org/data/download/12881621/description.xml -1 12881622 predictions https://api.openml.org/data/download/12881622/predictions.arff area_under_roc_curve 0.992417505087374 [0.996002,0.999882,0.982746,0.994157,0.962768,0.993564,0.991314,0.99463,0.999013,0.998973,0.995657,0.984089,0.999289,0.996012,0.97635,0.999013,0.985629,0.995696,0.999684,0.972007,0.999763,0.97564,0.997197,0.998697,0.952029,0.995183,0.997315,0.959531,0.999882,0.999605,0.992735,1,0.957833,0.998737,0.996012,0.988155,0.992577,0.999684,0.98784,0.995302,0.999447,0.993683,0.98855,0.96802,0.988195,0.998776,0.987958,0.999566,0.981917,0.998697,0.996486,0.998816,0.998105,0.997552,0.986734,0.996447,1,0.989261,0.99921,0.998737,0.999803,0.997868,0.999447,0.997434,0.996368,0.99775,0.999526,0.992143,0.999013,0.9756,0.998776,0.981917,1,0.997276,0.999724,0.999171,0.991314,0.998105,0.999605,0.992735,0.99692,0.989182,0.979232,0.995696,0.983852,0.99775,0.966875,0.998934,0.99925,0.998855,0.991393,0.97868,0.999921,0.998895,0.995539,0.99542,0.999447,0.998579,0.998658,0.998973] average_cost 0 f_measure 0.7297870388216392 [0.785714,1,0.666667,0.666667,0.611111,0.6875,0.583333,0.774194,0.833333,0.8,0.875,0.642857,0.909091,0.875,0.608696,0.857143,0.342857,0.857143,0.909091,0.823529,0.941176,0.454545,0.75,0.882353,0.285714,0.588235,0.8,0.2,0.941176,0.8,0.583333,0.941176,0.64,0.736842,0.733333,0.210526,0.717949,0.909091,0.592593,0.705882,0.857143,0.5,0.466667,0.5,0.580645,0.756757,0.578947,0.967742,0.6875,0.769231,0.736842,0.709677,0.764706,0.756757,0.645161,0.615385,0.941176,0.75,0.882353,0.875,0.882353,0.787879,0.833333,0.764706,0.83871,0.740741,0.882353,0.685714,0.777778,0.56,0.848485,0.56,0.888889,0.75,0.896552,0.8,0.75,0.823529,0.882353,0.571429,0.8,0.714286,0.62069,0.545455,0.6,0.8,0.56,0.789474,0.909091,0.780488,0.933333,0.1,0.941176,0.833333,0.736842,0.666667,0.8,0.810811,0.83871,0.8125] kappa 0.7422628524472292 kb_relative_information_score 1111.7342139642021 mean_absolute_error 0.013574267000158393 mean_prior_absolute_error 0.019799992711748222 number_of_instances 1599 [15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16] precision 0.7512767675978983 [0.846154,1,1,0.818182,0.55,0.6875,0.875,0.8,0.75,0.857143,0.875,0.75,0.882353,0.875,1,0.789474,0.315789,0.789474,0.882353,0.777778,0.888889,0.833333,0.75,0.833333,0.6,0.555556,0.857143,0.5,0.888889,0.666667,0.875,0.888889,0.888889,0.636364,0.785714,0.666667,0.608696,0.882353,0.727273,0.666667,0.789474,0.583333,0.5,0.75,0.6,0.666667,0.5,1,0.6875,0.652174,0.636364,0.733333,0.722222,0.666667,0.666667,0.521739,0.888889,0.75,0.833333,0.875,0.833333,0.764706,0.75,0.722222,0.866667,0.909091,0.833333,0.631579,0.7,0.777778,0.823529,0.777778,0.8,0.625,1,0.736842,0.75,0.777778,0.833333,0.666667,0.736842,0.833333,0.692308,0.529412,0.642857,0.736842,0.777778,0.681818,0.882353,0.64,1,0.25,0.888889,0.75,0.636364,0.6,0.736842,0.714286,0.866667,0.8125] predictive_accuracy 0.7448405253283302 prior_entropy 6.6438309601245775 recall 0.7448405253283302 [0.733333,1,0.5,0.5625,0.6875,0.6875,0.4375,0.75,0.9375,0.75,0.875,0.5625,0.9375,0.875,0.4375,0.9375,0.375,0.9375,0.9375,0.875,1,0.3125,0.75,0.9375,0.1875,0.625,0.75,0.125,1,1,0.4375,1,0.5,0.875,0.6875,0.125,0.875,0.9375,0.5,0.75,0.9375,0.4375,0.4375,0.375,0.5625,0.875,0.6875,0.9375,0.6875,0.9375,0.875,0.6875,0.8125,0.875,0.625,0.75,1,0.75,0.9375,0.875,0.9375,0.8125,0.9375,0.8125,0.8125,0.625,0.9375,0.75,0.875,0.4375,0.875,0.4375,1,0.9375,0.8125,0.875,0.75,0.875,0.9375,0.5,0.875,0.625,0.5625,0.5625,0.5625,0.875,0.4375,0.9375,0.9375,1,0.875,0.0625,1,0.9375,0.875,0.75,0.875,0.9375,0.8125,0.8125] relative_absolute_error 0.6855692927656571 root_mean_prior_squared_error 0.09949872432040595 root_mean_squared_error 0.07393833925926008 root_relative_squared_error 0.7431084143467379 total_cost 0 area_under_roc_curve 0.9952969508797069 [1,1,0.974843,1,0.996835,0.968354,0.990506,1,1,1,1,0.990506,1,1,0.968354,1,0.993671,1,1,1,1,0.962025,1,0.993671,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.971519,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,0.987342,1,1,1,1,1,0.924528,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.943038,1,1,0.993671,0.987342,0.971519,1,1,0.981013,0.996835,1,1,1,1] area_under_roc_curve 0.9896870770639277 [1,1,1,0.996835,0.990506,0.993671,1,1,1,0.996835,1,1,1,1,1,1,0.974684,1,1,0.93038,1,0.924051,0.996835,1,0.917722,0.981013,0.949686,1,1,1,0.984177,1,1,1,0.974843,0.990506,1,1,0.990506,1,1,0.993711,0.962264,0.81962,1,1,0.946203,1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,0.993671,1,0.981132,1,0.996835,1,0.924528,1,0.946203,1,0.996835,1,1,1,0.996835,1,0.993671,1,0.993711,1,0.996835,0.955975,1,1,1,0.993671,0.993671,1,1,1,1,1,0.981132,1,0.981013,1,1,1,1] area_under_roc_curve 0.995136981530133 [1,1,0.993671,1,1,1,1,1,1,1,1,0.993711,1,0.984177,1,1,0.996835,0.996835,1,1,1,0.981013,0.996835,1,0.981132,0.996835,1,0.977848,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,0.993711,0.993711,1,0.981013,1,0.987342,1,1,1,1,0.990506,1,1,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.955696,1,1,1,1,1,1,1,0.990506,1,0.955696,1,0.990506,0.993711,0.990506,0.905063,1,1,1,1,0.977848,1,1,1,1,1,1,1,1] area_under_roc_curve 0.990161511822307 [1,1,0.990506,1,1,0.993711,0.958861,0.990506,1,1,1,1,1,0.987342,0.844937,1,0.987342,1,1,1,1,1,1,1,0.937107,0.996835,1,0.85443,1,1,1,1,0.971519,1,0.996835,0.987421,1,1,0.874214,1,1,0.993711,0.962264,0.996835,0.968354,0.996835,1,1,1,1,1,1,1,0.990506,0.987342,0.993671,1,1,1,1,1,1,1,0.981132,1,1,1,1,1,0.993671,1,0.939873,1,1,1,1,0.933544,0.993711,1,1,1,0.996835,1,0.996835,1,1,1,1,0.993671,1,1,0.987342,1,1,1,1,1,1,1,0.993711] area_under_roc_curve 0.994593135498766 [1,1,0.977848,1,0.987421,1,1,0.971519,1,1,1,0.993711,0.993711,1,0.981132,1,0.977848,1,1,1,1,0.874214,1,1,0.993711,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.955975,1,0.996835,0.987342,0.990506,0.993671,1,1,1,1,1,0.990506,0.996835,1,1,0.955696,0.996835,1,0.996835,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,0.996835,1,0.993711,1,1,0.993671,0.993671,0.977848,0.968553,1,1,1,0.955696,1,1,0.993711,0.962264,0.990506,1,0.993711,1,1,1,1,0.996835,1] area_under_roc_curve 0.9960903291935355 [1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,0.993711,1,1,0.899371,1,1,0.987421,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,1,0.946203,1,0.993671,1,1,1,0.993671,1,1,1,0.993671,0.993671,1,0.955696,1,1,1,0.981132,0.996835,1,1,1,1,0.990506,0.996835,0.996835,1,1,1,1,1,1,1,1,1,0.996835,0.990506,0.993711,0.987342,0.993711,0.949367,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1] area_under_roc_curve 0.9928133209935514 [0.993711,0.996835,1,0.974843,1,0.990506,1,1,1,1,0.974684,0.905063,1,1,1,1,1,1,1,1,1,1,0.981132,1,0.946203,1,1,1,1,1,0.993711,1,0.993671,1,0.996835,0.977848,0.990506,1,1,1,1,1,0.987342,1,0.996835,0.996835,0.993711,1,1,1,1,1,0.993671,1,1,0.987421,1,0.987342,1,1,0.996835,1,1,0.993671,0.974843,1,1,1,0.990506,0.949686,1,1,1,0.974684,1,1,1,0.993671,1,1,1,1,0.908228,0.987421,1,1,1,0.993671,1,1,1,0.849057,1,1,0.993711,1,1,1,1,1] area_under_roc_curve 0.9885822187723903 [0.930818,1,0.924051,1,1,1,1,1,0.996835,1,0.987342,1,1,1,1,1,0.981132,0.974684,1,0.901899,1,1,1,1,0.924051,0.993711,0.996835,0.949686,1,1,0.974843,1,0.718354,1,1,0.993671,1,0.990506,0.993671,1,1,0.993671,1,0.981132,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.968553,1,1,1,0.993711,1,0.990506,0.996835,0.96519,1,1,1,0.987421,0.939873,0.993711,0.90566,1,1,1,1,0.987421,1,1,1,0.984177,1,1,1,1] area_under_roc_curve 0.9939184280710132 [0.974843,1,1,0.977848,0.993671,1,1,0.993671,0.990506,1,1,1,1,1,1,0.993671,0.993711,1,1,1,1,0.990506,0.993711,1,0.946203,1,1,0.981013,1,1,0.996835,1,1,1,0.987421,1,0.955696,1,1,1,0.981132,1,0.996835,0.987421,0.993711,0.993711,1,0.993711,0.882911,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,0.996835,0.987342,1,1,1,1,0.867925,0.993671,0.990506,1,1,1,1,0.993671,1,1,0.981132,0.996835,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.993671] area_under_roc_curve 0.9939951634748544 [1,1,1,0.984076,0.780255,1,0.984076,1,1,1,1,1,1,1,1,1,0.974684,1,1,1,1,0.987261,1,1,0.923567,1,1,0.993631,1,1,1,1,1,0.987342,1,1,1,1,0.996815,0.996815,1,0.984076,0.984076,0.987342,1,1,0.962025,1,0.987261,1,0.993631,1,1,1,1,1,1,1,1,0.996815,1,1,1,1,1,0.996815,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993631,1,0.993631,1,1,1,1,1,1,1,0.993671,1,1,0.996815,1,1,1,0.993671,1] 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 0.7536906923502013 kappa 0.6589967241583455 kappa 0.7410287789664838 kappa 0.7347019344650613 kappa 0.7283960364770439 kappa 0.7788658979624072 kappa 0.7725387987205308 kappa 0.7409981048641819 kappa 0.7284281992579142 kappa 0.783863745402207 kb_relative_information_score 111.56298281222404 kb_relative_information_score 108.29024290771922 kb_relative_information_score 112.52564507331083 kb_relative_information_score 108.97322233875339 kb_relative_information_score 110.83499908006162 kb_relative_information_score 115.49467966301444 kb_relative_information_score 111.75427220826342 kb_relative_information_score 109.74217057900835 kb_relative_information_score 110.01161277631438 kb_relative_information_score 112.54438652553252 mean_absolute_error 0.01355189894191669 mean_absolute_error 0.013839312461032557 mean_absolute_error 0.013582838167570836 mean_absolute_error 0.01376281612119217 mean_absolute_error 0.013560461475719713 mean_absolute_error 0.013000821554114177 mean_absolute_error 0.013336438596404048 mean_absolute_error 0.013785018915549238 mean_absolute_error 0.013921323169365087 mean_absolute_error 0.013400655527012258 mean_prior_absolute_error 0.01980002942907592 mean_prior_absolute_error 0.01980002942907592 mean_prior_absolute_error 0.019800029429075917 mean_prior_absolute_error 0.019800029429075917 mean_prior_absolute_error 0.01980002942907591 mean_prior_absolute_error 0.01979995585638609 mean_prior_absolute_error 0.01979995585638609 mean_prior_absolute_error 0.01979995585638609 mean_prior_absolute_error 0.019799955856386102 mean_prior_absolute_error 0.01979995631910742 number_of_instances 160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,1,2,2,2,2,1,1,2,2,1,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,2,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1] number_of_instances 160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,2,2,2,2,2,2,1,2,2,2,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,1,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,1,2,1,2,2,2,2,2,2,2,1,1,2,2,1,1,2,1] number_of_instances 160 [2,1,2,2,2,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,1,1,2,2,2,1,2,1,1,1,1,2,1,1,1,2,2,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,2,1,1,2,2,1] number_of_instances 160 [2,1,2,2,2,1,2,2,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,1,1,1,2,2,2,2,2,1,1,1,1,2,1,1,1,2,1,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,1,1,2,2,2,1] number_of_instances 160 [2,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,2,2,1,2,2,1,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,2,1,1,2,2,2,1,2,1,1,2,2,1,1,1,2,2,2,2] number_of_instances 160 [1,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,1,2,1,2,2,2,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,1,2,1,2,2,2,1,2,1,1,2,2,2,1,1,2,2,2,2] number_of_instances 160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,1,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,1,2,2,2,1,2] number_of_instances 160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,1,1,1,2,2,1,2,1,1,1,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,2,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,2,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,2,2,2,2,1,2] number_of_instances 160 [1,2,1,2,2,2,2,2,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,1,1,2,2,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,1,2,2,2,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2] number_of_instances 159 [1,2,1,2,2,2,2,1,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,2,1,2,1,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,2,1,1,1,2] predictive_accuracy 0.75625 predictive_accuracy 0.6625 predictive_accuracy 0.74375 predictive_accuracy 0.7375 predictive_accuracy 0.73125 predictive_accuracy 0.78125 predictive_accuracy 0.775 predictive_accuracy 0.74375 predictive_accuracy 0.73125 predictive_accuracy 0.7861635220125787 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 recall 0.75625 [1,1,0,1,0.5,0.5,0,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0.5,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0,0.5,1,1,1,0,1,1,0.5,1,0.5,1,1,1,0,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,0,1,1,0.5,0.5,1,1,1,1] recall 0.6625 [1,1,1,0,0.5,0,0.5,1,1,0,1,1,1,1,0,1,0.5,1,1,0.5,1,0.5,1,1,0.5,0,0,0,1,1,0,1,1,1,0,0,1,1,0,0.5,1,0,0,0.5,1,1,0,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,1,0,0.5,0,1,0.5,1,0.5,1,1,1,0.5,0.5,1,1,1,1,0.5,0,0.5,1,1,0,1,1,1,1,0,1,1,1,0.5,0,1,0,1] recall 0.74375 [1,1,0.5,0.5,1,1,1,1,1,1,1,0,1,0.5,0.5,1,0.5,1,1,1,1,0,0.5,1,0,0,1,0,1,1,0,1,0,0.5,1,1,1,1,1,1,1,1,0,0,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0,1,0,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,0,1,0.5,0,1,1,1,1,0,1,1,1,1,1,1,1,1] recall 0.7375 [0.5,1,0.5,1,1,0,0,0,1,1,1,0,1,0.5,0,1,0.5,1,0.5,1,1,0.5,1,1,0,0.5,1,0,1,1,0.5,1,0.5,1,0.5,0,1,1,0,1,1,0,0,1,0.5,0.5,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,0,1,1,0.5,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1] recall 0.73125 [0.5,1,0.5,1,1,1,1,0.5,1,0,1,1,0,1,0,1,0,1,1,1,1,0,0.5,1,0,0.5,1,1,1,1,1,1,0.5,1,0.5,0,1,1,1,0,1,1,0.5,0,0.5,0.5,1,1,1,1,0.5,0.5,1,0,0,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,0,1,0,0,1,0.5,1,0.5,1,1,1,0,0.5,1,0,1,1,0.5,1,1,1] recall 0.78125 [1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0.5,0,1,1,1,1,1,0,0.5,1,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,1,0.5,0.5,0.5,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0,0,1,0,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,0.5] recall 0.775 [1,1,0,0,1,0.5,0,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0,1,0,1,1,0,1,1,0,1,0.5,1,0.5,0,0.5,1,1,1,1,0.5,0.5,1,0,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,0.5,0,1,1,1,0.5,1,1,1,0.5,1,1,1,1,0,1,0,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1] recall 0.74375 [0,1,0.5,1,1,1,1,1,0.5,0,0.5,0.5,1,1,1,1,0,0.5,1,0.5,1,0,1,1,0,1,0.5,0,1,1,0,1,0,0.5,1,0,1,0.5,0,1,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,0,1,0.5,1,0,1,1,1,0,0.5,0,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1] recall 0.73125 [0,1,1,0,0.5,1,0,1,1,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,0.5,0.5,1,0.5,1,0,0.5,0,0,0,1,1,1,0,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0,1,0] recall 0.7861635220125787 [1,1,1,0.5,0,1,0.5,0,1,1,1,0.5,1,1,0.5,1,0,1,1,1,1,0,0,1,0.5,1,1,0,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,0,1,1,0,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,0.5,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,1,0,1] relative_absolute_error 0.6844383232085511 relative_absolute_error 0.6989541359322337 relative_absolute_error 0.6860009080403047 relative_absolute_error 0.6950906901674485 relative_absolute_error 0.6848707737679659 relative_absolute_error 0.6566086130904688 relative_absolute_error 0.6735590065521605 relative_absolute_error 0.6962146287363135 relative_absolute_error 0.7030986973071977 relative_absolute_error 0.6768022772898904 root_mean_prior_squared_error 0.09949890883178135 root_mean_prior_squared_error 0.09949890883178135 root_mean_prior_squared_error 0.09949890883178135 root_mean_prior_squared_error 0.09949890883178135 root_mean_prior_squared_error 0.09949890883178135 root_mean_prior_squared_error 0.09949853911503082 root_mean_prior_squared_error 0.09949853911503082 root_mean_prior_squared_error 0.09949853911503082 root_mean_prior_squared_error 0.09949853911503082 root_mean_prior_squared_error 0.0994985414402977 root_mean_squared_error 0.07347602308959622 root_mean_squared_error 0.07581171796161507 root_mean_squared_error 0.0738853045193491 root_mean_squared_error 0.07509492803154362 root_mean_squared_error 0.07423766715413144 root_mean_squared_error 0.07141113219709044 root_mean_squared_error 0.07292656734221831 root_mean_squared_error 0.07445782154370838 root_mean_squared_error 0.07533574528482616 root_mean_squared_error 0.07262673548391074 root_relative_squared_error 0.7384605917017549 root_relative_squared_error 0.7619351694578558 root_relative_squared_error 0.7425740180152518 root_relative_squared_error 0.7547311715599162 root_relative_squared_error 0.7461153898646462 root_relative_squared_error 0.7177103586870922 root_relative_squared_error 0.7329410862797442 root_relative_squared_error 0.7483308016977743 root_relative_squared_error 0.7571542854285537 root_relative_squared_error 0.7299276394668469 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 5597.289563000004 usercpu_time_millis 5687.494020999992 usercpu_time_millis 5754.2060580000225 usercpu_time_millis 5789.806242999986 usercpu_time_millis 5788.164628999964 usercpu_time_millis 5630.090203000009 usercpu_time_millis 5609.618050999985 usercpu_time_millis 5815.891245999978 usercpu_time_millis 5797.045086999987 usercpu_time_millis 5665.105815999994 usercpu_time_millis_testing 37.32713499999818 usercpu_time_millis_testing 41.415656999987505 usercpu_time_millis_testing 39.46574700000838 usercpu_time_millis_testing 39.039390999988655 usercpu_time_millis_testing 40.1866369999766 usercpu_time_millis_testing 38.56854500000395 usercpu_time_millis_testing 39.06189299999596 usercpu_time_millis_testing 39.26834699998949 usercpu_time_millis_testing 38.877896000002465 usercpu_time_millis_testing 39.08026899998163 usercpu_time_millis_training 5559.962428000006 usercpu_time_millis_training 5646.0783640000045 usercpu_time_millis_training 5714.740311000014 usercpu_time_millis_training 5750.766851999998 usercpu_time_millis_training 5747.977991999988 usercpu_time_millis_training 5591.521658000005 usercpu_time_millis_training 5570.5561579999885 usercpu_time_millis_training 5776.622898999989 usercpu_time_millis_training 5758.167190999984 usercpu_time_millis_training 5626.025547000012