10208595 1 Jan van Rijn 9954 Supervised Classification 9666 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,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(4) 8134027 copy true 9559 fill_value -1 9559 missing_values NaN 9559 strategy "constant" 9559 verbose 0 9559 n_jobs null 9606 remainder "passthrough" 9606 sparse_threshold 0.3 9606 transformer_weights null 9606 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}] 9606 memory null 9607 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"}}] 9607 axis 0 9608 copy true 9608 missing_values "NaN" 9608 strategy "mean" 9608 verbose 0 9608 copy true 9609 with_mean true 9609 with_std true 9609 memory null 9610 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"}}] 9610 categorical_features null 9611 categories null 9611 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 9611 handle_unknown "ignore" 9611 n_values null 9611 sparse true 9611 threshold 0.0 9612 memory null 9666 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": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}] 9666 criterion "friedman_mse" 9667 init null 9667 learning_rate 0.001309934740014382 9667 loss "deviance" 9667 max_depth 17 9667 max_features 0.8918767676071436 9667 max_leaf_nodes null 9667 min_impurity_decrease 0.41724437242561563 9667 min_impurity_split null 9667 min_samples_leaf 8 9667 min_samples_split 10 9667 min_weight_fraction_leaf 0.07771601367523312 9667 n_estimators 642 9667 n_iter_no_change 700 9667 presort "auto" 9667 random_state 53540 9667 subsample 0.5317701356531108 9667 tol 2.8680717303170383e-05 9667 validation_fraction 0.8079103924062544 9667 verbose 0 9667 warm_start false 9667 openml-python Sklearn_0.20.3. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 21338106 description https://api.openml.org/data/download/21338106/description.xml -1 21338107 predictions https://api.openml.org/data/download/21338107/predictions.arff area_under_roc_curve 0.5390013415404039 [0.838699,0.382418,0.461411,0.283144,0.209793,0.744437,0.210898,0.816248,0.55524,0.180082,0.567472,0.164457,0.586766,0.479127,0.267835,0.744121,0.859967,0.630642,0.404514,0.819444,0.618253,0.354246,0.374882,0.972932,0.582663,0.480508,0.629932,0.685369,0.663431,0.691604,0.67223,0.352904,0.805279,0.143821,0.579664,0.1948,0.29794,0.511324,0.577652,0.316525,0.633404,0.561001,0.143032,0.75801,0.11194,0.683988,0.027265,0.392045,0.909446,0.47968,0.825008,0.222854,0.427675,0.413116,0.806384,0.275726,0.957505,0.57047,0.389954,0.462792,0.287247,0.766493,0.065144,0.576113,0.397569,0.75142,0.704901,0.354246,0.722301,0.866911,0.555595,0.103022,0.635732,0.548887,0.382655,0.510851,0.672861,0.660354,0.700047,0.540128,0.790483,0.989149,0.965278,0.336687,0.682331,0.65191,0.447562,0.973248,0.856889,0.602312,0.384509,0.385614,0.18679,0.414536,0.596828,0.618134,0.559067,0.632773,0.723603,0.535985] average_cost 0 kappa 0.014520202020202022 kb_relative_information_score 51.068279242135276 mean_absolute_error 0.019759386591557262 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 0.024375 prior_entropy 6.6438561897747395 recall 0.024375 [0.1875,0,0,0,0,0,0,0.0625,0,0,0,0,0,0,0,0,0.0625,0,0,0,0,0,0,0.4375,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0625,0.0625,0.75,0,0,0,0,0.8125,0,0,0,0,0,0,0,0,0,0,0,0] relative_absolute_error 0.9979488177554154 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.09972642664858482 root_relative_squared_error 1.0022882996250166 total_cost 0 area_under_roc_curve 0.5268260886872064 [0.484277,0.28481,0.522152,0.389937,0.35443,0.698113,0.123418,0.836478,0.443038,0.037736,0.35443,0.170886,0.636076,0.148734,0.348101,0.874214,0.936709,0.610759,0.550633,0.874214,0.553459,0.201258,0.194969,0.981132,0.672956,0.189873,0.617089,0.636076,0.702532,0.496855,0.455696,0.287975,0.746835,0.177215,0.575949,0.081761,0.119497,0.666667,0.786164,0.253165,0.81761,0.861635,0.443038,0.829114,0.003165,0.759494,0.018868,0.09434,0.968354,0.708861,0.746835,0.227848,0.716981,0.420886,0.754717,0.08805,0.93038,0.396226,0.08805,0.689873,0.18239,0.484277,0.044304,0.629747,0.54717,0.829114,0.863924,0.163522,0.710692,0.962264,0.670886,0.044025,0.718354,0.699367,0.050314,0.278481,0.506329,0.746835,0.806962,0.737342,0.721519,0.993671,0.955975,0.333333,0.756329,0.613924,0.496835,0.987342,0.946203,0.339623,0.414557,0.201258,0.139241,0.25,0.531646,0.376582,0.339623,0.518987,0.594937,0.735849] area_under_roc_curve 0.5497497213597644 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[0.993711,0.155063,0.836478,0.424051,0.341772,0.81761,0.189873,0.176101,0.879747,0.006289,0.63522,0.232704,0.5,0.487342,0.278481,0.754717,0.748428,0.660377,0.300633,0.792453,0.889241,0.325949,0.591195,0.924051,0.503165,0.283019,0.72327,0.528481,0.541139,0.503145,0.892405,0.107595,0.893082,0.132075,0.601266,0.132911,0.240506,0.161392,0.142405,0.496855,0.383648,0.797468,0.189873,0.800633,0.25,0.806962,0,0.443038,0.974843,0.773585,0.658228,0.218354,0.246835,0.509494,0.981013,0.72956,0.996835,0.522013,0.348101,0.398734,0.411392,0.857595,0.018987,0.446203,0.408805,0.893082,0.689873,0.232704,0.746835,0.987421,0.484277,0.132075,0.712025,0.348101,0.174051,0.838608,0.937107,0.924528,0.503145,0.420886,0.990506,0.968553,0.987421,0.18239,0.689873,0.71519,0.506329,0.996835,0.861635,0.835443,0.560127,0.155063,0.212025,0.322785,0.792453,0.873418,0.415094,0.981013,0.806962,0.81761] area_under_roc_curve 0.5457716344240108 [1,0.417722,0.628931,0.208861,0.041139,0.880503,0.139241,0.037736,0.93038,0.031447,0.257862,0,0.655063,0.313291,0.310127,0.333333,0.805031,0.213836,0.43038,0.974843,0.768987,0.642405,0.352201,0.993671,0.756329,0.383648,0.836478,0.620253,0.503165,0.924528,0.889241,0.436709,1,0.056604,0.626582,0.278481,0.373418,0.477848,0.636076,0.056604,0.679245,0.775316,0.113924,0.518987,0.056962,0.575949,0.012579,0.351266,0.968553,0.283019,0.857595,0.161392,0.515823,0.553797,0.832278,0.503145,0.977848,0.660377,0.303797,0.620253,0.398734,0.487342,0.107595,0.607595,0.515723,0.805031,0.901899,0.220126,0.316456,0.823899,0.767296,0.018868,0.601266,0.607595,0.531646,0.651899,0.666667,0.955975,0.622642,0.28481,0.993671,0.968553,0.955975,0.044025,0.689873,0.579114,0.405063,0.996835,0.830189,0.522152,0.455696,0.607595,0.047468,0.64557,0.251572,0.794304,0.943396,0.962025,0.85443,0.622642] 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 0.0026039611773060844 kappa 0.02173484280698986 kappa 0.014451551764984602 kappa 0.026702480644651604 kappa 0.015501124127322212 kappa 0.015268079062610962 kappa 0.009541834240201877 kappa 0.009307461744754693 kappa 0.00891659433441174 kappa 0.021387420093126035 kb_relative_information_score 4.310771352692405 kb_relative_information_score 4.973235713505513 kb_relative_information_score 5.740043745167367 kb_relative_information_score 5.33071120619258 kb_relative_information_score 5.022154287977711 kb_relative_information_score 5.243641648791545 kb_relative_information_score 4.7823423678805606 kb_relative_information_score 4.519367900253823 kb_relative_information_score 5.435344475146963 kb_relative_information_score 5.710666544526855 mean_absolute_error 0.019770535734159177 mean_absolute_error 0.019763471084247645 mean_absolute_error 0.019748054770706153 mean_absolute_error 0.019737180086614785 mean_absolute_error 0.019764758887294183 mean_absolute_error 0.0197551164931485 mean_absolute_error 0.019756216094124306 mean_absolute_error 0.019779309949569328 mean_absolute_error 0.019761358125088925 mean_absolute_error 0.019757864690619638 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 0.0125 predictive_accuracy 0.03125 predictive_accuracy 0.025 predictive_accuracy 0.0375 predictive_accuracy 0.025 predictive_accuracy 0.025 predictive_accuracy 0.01875 predictive_accuracy 0.01875 predictive_accuracy 0.01875 predictive_accuracy 0.03125 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 0.0125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.03125 [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.025 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.0375 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.025 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.025 [0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.01875 [0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.01875 [0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.01875 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.03125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0] relative_absolute_error 0.9985119057656132 relative_absolute_error 0.9981551052650309 relative_absolute_error 0.9973765035710161 relative_absolute_error 0.9968272771017551 relative_absolute_error 0.9982201458229369 relative_absolute_error 0.9977331562196196 relative_absolute_error 0.9977886916224381 relative_absolute_error 0.9989550479580452 relative_absolute_error 0.9980483901560047 relative_absolute_error 0.9978719540716972 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 0.099787231496248 root_mean_squared_error 0.09976268147656854 root_mean_squared_error 0.09965330453272168 root_mean_squared_error 0.09961843057349312 root_mean_squared_error 0.09971652943219207 root_mean_squared_error 0.09964085463109182 root_mean_squared_error 0.0997554252536931 root_mean_squared_error 0.09982247478357006 root_mean_squared_error 0.09973386118186106 root_mean_squared_error 0.09977326376508572 root_relative_squared_error 1.0028994113375427 root_relative_squared_error 1.0026526743561106 root_relative_squared_error 1.0015533947092747 root_relative_squared_error 1.0012028982313497 root_relative_squared_error 1.0021888288576117 root_relative_squared_error 1.001428268489932 root_relative_squared_error 1.0025797465722528 root_relative_squared_error 1.00325361970247 root_relative_squared_error 1.002363019495831 root_relative_squared_error 1.002759030357428 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 51619.122276999406 usercpu_time_millis 51344.77216399955 usercpu_time_millis 51391.84886900057 usercpu_time_millis 51217.77390900024 usercpu_time_millis 51468.51283000069 usercpu_time_millis 51515.2197740008 usercpu_time_millis 51366.643163000845 usercpu_time_millis 51490.53986299987 usercpu_time_millis 51614.85585199898 usercpu_time_millis 51295.674287999645 usercpu_time_millis_testing 43.92520599958516 usercpu_time_millis_testing 43.43993299971771 usercpu_time_millis_testing 46.96090700053901 usercpu_time_millis_testing 43.05690599994705 usercpu_time_millis_testing 47.96277100012958 usercpu_time_millis_testing 41.00618200027384 usercpu_time_millis_testing 51.26068900062819 usercpu_time_millis_testing 43.68790699936653 usercpu_time_millis_testing 43.348790999516496 usercpu_time_millis_testing 46.97860899977968 usercpu_time_millis_training 51575.19707099982 usercpu_time_millis_training 51301.33223099983 usercpu_time_millis_training 51344.88796200003 usercpu_time_millis_training 51174.717003000296 usercpu_time_millis_training 51420.55005900056 usercpu_time_millis_training 51474.213592000524 usercpu_time_millis_training 51315.38247400022 usercpu_time_millis_training 51446.8519560005 usercpu_time_millis_training 51571.507060999465 usercpu_time_millis_training 51248.695678999866