10225122 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) 8150554 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 "most_frequent" 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 0.000885008035983206 9667 loss "deviance" 9667 max_depth 2 9667 max_features 0.4780243161632962 9667 max_leaf_nodes null 9667 min_impurity_decrease 0.1517668984997802 9667 min_impurity_split null 9667 min_samples_leaf 13 9667 min_samples_split 15 9667 min_weight_fraction_leaf 0.049379839967862105 9667 n_estimators 161 9667 n_iter_no_change 715 9667 presort "auto" 9667 random_state 1672 9667 subsample 0.8363220673646695 9667 tol 0.00013257197861012826 9667 validation_fraction 0.6547480164024817 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 21371160 description https://api.openml.org/data/download/21371160/description.xml -1 21371161 predictions https://api.openml.org/data/download/21371161/predictions.arff area_under_roc_curve 0.5829943970959597 [0.833886,0.360085,0.72167,0.466974,0.390349,0.666588,0.1744,0.939078,0.700402,0.400371,0.124132,0.334833,0.811198,0.348643,0.187224,0.851168,0.790601,0.318142,0.654119,0.859257,0.927951,0.460701,0.407,0.980469,0.618292,0.685014,0.413471,0.750355,0.493174,0.487137,0.656408,0.234848,0.780264,0.274384,0.503117,0.140112,0.384746,0.421402,0.656289,0.147214,0.702809,0.615294,0.116122,0.467211,0.346473,0.80962,0.062697,0.21441,0.939749,0.645912,0.899266,0.713108,0.459557,0.846394,0.575008,0.358546,0.964173,0.904435,0.467172,0.599471,0.304924,0.606416,0.126065,0.628235,0.325915,0.649266,0.56325,0.186198,0.787366,0.755879,0.623698,0.143782,0.497554,0.540246,0.456439,0.536774,0.664457,0.685014,0.566406,0.518821,0.90913,0.983349,0.962753,0.984809,0.969855,0.980232,0.999369,0.969539,0.965515,0.727746,0.361664,0.079269,0.281645,0.486624,0.577573,0.704111,0.835425,0.862295,0.532592,0.896346] average_cost 0 kappa 0.041035353535353536 kb_relative_information_score 50.24735474878867 mean_absolute_error 0.019765229497605622 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 0.050625 prior_entropy 6.6438561897747395 recall 0.050625 [0,0,0,0,0,0,0,0.0625,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0,0,0.0625,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0625,0,0,0,0,0,0,0,0,0,0,0.3125,0,0.125,0,0,0,0,0,0.5625,0,0,0,0,0,0,0,0,0,0,0,0.0625,0,0,0,0,0,0,0,0,0,0,0,0,0.1875,0.1875,0.0625,0.625,0.5625,1,0.125,0.625,0,0,0,0,0,0,0,0.125,0,0,0.1875] relative_absolute_error 0.9982439140204842 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.09938096653519517 root_relative_squared_error 0.9988162948488133 total_cost 0 area_under_roc_curve 0.5662417403073003 [0.786164,0.259494,0.696203,0.584906,0.170886,0.685535,0.063291,0.924528,0.699367,0.169811,0.044304,0.357595,0.696203,0.110759,0.268987,0.654088,0.892405,0.338608,0.446203,0.836478,0.924528,0.283019,0.402516,0.987421,0.742138,0.294304,0.351266,0.731013,0.481013,0.628931,0.332278,0.253165,0.835443,0.449367,0.436709,0.106918,0.257862,0.333333,0.534591,0.18038,0.836478,0.72956,0.237342,0.64557,0.886076,0.863924,0.119497,0.044025,0.984177,0.683544,0.962025,0.794304,0.490566,0.89557,0.503145,0.09434,0.968354,0.981132,0.238994,0.712025,0.220126,0.528302,0.117089,0.718354,0.465409,0.537975,0.550633,0.012579,0.930818,0.842767,0.610759,0.176101,0.389241,0.541139,0.144654,0.53481,0.455696,0.712025,0.642405,0.64557,1,1,0.993711,1,0.968354,0.993671,1,0.993671,0.946203,0.345912,0.332278,0.025157,0.28481,0.408228,0.503165,0.484177,0.874214,0.696203,0.506329,0.968553] area_under_roc_curve 0.5837460691823899 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[0.786164,0.227848,0.427673,0.297468,0.484177,0.918239,0.098101,0.924528,0.699367,0.138365,0.257862,0.371069,0.905063,0.234177,0.126582,0.716981,0.91195,0.327044,0.791139,0.861635,0.971519,0.392405,0.716981,0.96519,0.626582,0.91195,0.446541,0.794304,0.522152,0.251572,0.908228,0.205696,0.597484,0.194969,0.525316,0.14557,0.256329,0.18038,0.373418,0.352201,0.553459,0.822785,0.142405,0.588608,0.079114,0.822785,0,0.243671,0.955975,0.767296,0.879747,0.905063,0.287975,0.914557,0.835443,0.72956,0.984177,0.716981,0.357595,0.629747,0.443038,0.53481,0.041139,0.525316,0.264151,0.968553,0.610759,0.27044,0.851266,0.886792,0.610063,0.792453,0.746835,0.357595,0.316456,0.629747,0.867925,0.81761,0.641509,0.360759,0.898734,0.987421,0.867925,0.981132,1,1,1,1,1,0.791139,0.490506,0.066456,0.376582,0.481013,0.742138,0.860759,0.968553,0.89557,0.455696,0.91195] area_under_roc_curve 0.5841585562455216 [0.918239,0.493671,0.91195,0.762658,0.762658,0.628931,0.101266,0.962264,0.493671,0.886792,0.037736,0.069182,0.613924,0.221519,0.212025,0.779874,0.811321,0.144654,0.75,0.91195,0.753165,0.753165,0.27044,1,0.598101,0.918239,0.440252,0.873418,0.357595,0.754717,0.924051,0.496835,0.792453,0.169811,0.5,0.205696,0.287975,0.31962,0.503165,0.08805,0.591195,0.613924,0.094937,0.221519,0.079114,0.655063,0.006289,0.126582,0.968553,0.540881,0.901899,0.85443,0.601266,0.765823,0.575949,0.440252,0.955696,0.930818,0.598101,0.753165,0.493671,0.424051,0.376582,0.591772,0.264151,0.855346,0.547468,0.157233,0.689873,0.836478,0.855346,0.012579,0.35443,0.563291,0.585443,0.379747,0.679245,0.779874,0.402516,0.642405,0.955696,0.918239,0.893082,0.993711,0.958861,1,1,0.977848,1,0.724684,0.297468,0.101266,0.139241,0.5,0.08805,0.93038,0.943396,0.867089,0.613924,0.81761] 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 0.05949187312608491 kappa 0.04716832307935006 kappa 0.040782519523546584 kappa 0.04701798674660776 kappa 0.008642803583408975 kappa 0.03390031177236671 kappa 0.04694280078895464 kappa 0.039911570802573924 kappa 0.053366465507040585 kappa 0.034357619028835155 kb_relative_information_score 5.1175355481178295 kb_relative_information_score 4.63275627985911 kb_relative_information_score 4.863917374896738 kb_relative_information_score 5.311267252151408 kb_relative_information_score 4.419518283887984 kb_relative_information_score 4.709093970649235 kb_relative_information_score 5.512551879071135 kb_relative_information_score 5.339997545718522 kb_relative_information_score 5.479107713162216 kb_relative_information_score 4.861608901274569 mean_absolute_error 0.019761416979568625 mean_absolute_error 0.01977212267959496 mean_absolute_error 0.019766138299330764 mean_absolute_error 0.019763450366152794 mean_absolute_error 0.019774248825367997 mean_absolute_error 0.019768702930216757 mean_absolute_error 0.019760838162529885 mean_absolute_error 0.019762311326427088 mean_absolute_error 0.019756412128916538 mean_absolute_error 0.01976665327795078 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 0.06875 predictive_accuracy 0.05625 predictive_accuracy 0.05 predictive_accuracy 0.05625 predictive_accuracy 0.01875 predictive_accuracy 0.04375 predictive_accuracy 0.05625 predictive_accuracy 0.05 predictive_accuracy 0.0625 predictive_accuracy 0.04375 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 0.06875 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0.5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,1,0,1,1,0,0.5,0,0,0,0,0,0,0,0,0,0,0] recall 0.05625 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.5,0,0.5,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0.5,0,0,0,0,0,0,0,0,0,0,0] recall 0.05 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,1,0,1,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0] recall 0.05625 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,1,0,1,0.5,1,0,0,0,0,0,0,0,0.5,0,0,0] recall 0.01875 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.04375 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,1] recall 0.05625 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0.5,1,0,1,0,0,0,0,0,0,0,0,0,0,0] recall 0.05 [0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.5] recall 0.0625 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0] recall 0.04375 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,1,0,1,0,0,0,0,0,0,0,1,0,0,0] relative_absolute_error 0.9980513626044742 relative_absolute_error 0.9985920545249962 relative_absolute_error 0.9982898130975117 relative_absolute_error 0.9981540588966041 relative_absolute_error 0.9986994356246447 relative_absolute_error 0.9984193399099355 relative_absolute_error 0.9980221294206996 relative_absolute_error 0.99809653163773 relative_absolute_error 0.9977985923695205 relative_absolute_error 0.9983158221187246 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 0.09936245145519512 root_mean_squared_error 0.09941577973706278 root_mean_squared_error 0.09938289826298195 root_mean_squared_error 0.09937146976653732 root_mean_squared_error 0.09942396233991535 root_mean_squared_error 0.09939918309581723 root_mean_squared_error 0.09936156365077871 root_mean_squared_error 0.09936580883406484 root_mean_squared_error 0.0993380142997366 root_mean_squared_error 0.09938850202711871 root_relative_squared_error 0.9986302112932876 root_relative_squared_error 0.9991661806922856 root_relative_squared_error 0.9988357094435587 root_relative_squared_error 0.9987208487325745 root_relative_squared_error 0.9992484189452263 root_relative_squared_error 0.9989993781717048 root_relative_squared_error 0.9986212885231771 root_relative_squared_error 0.9986639542205299 root_relative_squared_error 0.9983846086399556 root_relative_squared_error 0.9988920293922109 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 16423.83531899395 usercpu_time_millis 16488.78212099953 usercpu_time_millis 16374.908371995843 usercpu_time_millis 16514.73223400535 usercpu_time_millis 16481.423110999458 usercpu_time_millis 16490.445625000575 usercpu_time_millis 16441.39085500501 usercpu_time_millis 16496.44260900095 usercpu_time_millis 16468.572632998985 usercpu_time_millis 16492.913734000467 usercpu_time_millis_testing 16.064455994637683 usercpu_time_millis_testing 15.728117999969982 usercpu_time_millis_testing 16.458259997307323 usercpu_time_millis_testing 16.067951000877656 usercpu_time_millis_testing 16.536704999452922 usercpu_time_millis_testing 18.116788000043016 usercpu_time_millis_testing 16.402418004872743 usercpu_time_millis_testing 16.015820001484826 usercpu_time_millis_testing 16.18296399828978 usercpu_time_millis_testing 17.01742700242903 usercpu_time_millis_training 16407.770862999314 usercpu_time_millis_training 16473.05400299956 usercpu_time_millis_training 16358.450111998536 usercpu_time_millis_training 16498.664283004473 usercpu_time_millis_training 16464.886406000005 usercpu_time_millis_training 16472.328837000532 usercpu_time_millis_training 16424.98843700014 usercpu_time_millis_training 16480.426788999466 usercpu_time_millis_training 16452.389669000695 usercpu_time_millis_training 16475.89630699804