8806923 1 Jan van Rijn 9954 Supervised Classification 7707 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1) 6782004 axis 0 7660 categorical_features [] 7660 copy true 7660 fill_empty 0 7660 missing_values "NaN" 7660 strategy "most_frequent" 7660 strategy_nominal "most_frequent" 7660 verbose 0 7660 categorical_features [] 7661 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 7661 handle_unknown "ignore" 7661 n_values "auto" 7661 sparse true 7661 threshold 0.0 7662 copy true 7663 with_mean false 7663 with_std true 7663 C 2562.1886440879002 7708 cache_size 200 7708 class_weight null 7708 coef0 -0.44508343195357725 7708 decision_function_shape null 7708 degree 3 7708 gamma 0.0050412128053365195 7708 kernel "poly" 7708 max_iter -1 7708 probability true 7708 random_state 2095 7708 shrinking false 7708 tol 0.00034599948612205764 7708 verbose false 7708 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 18529819 description https://api.openml.org/data/download/18529819/description.xml -1 18529820 predictions https://api.openml.org/data/download/18529820/predictions.arff area_under_roc_curve 0.9855689709595958 [0.959083,0.998343,0.998816,0.99929,0.997711,0.989149,0.992582,0.996488,0.993647,0.999882,0.996765,0.997593,0.992306,0.922901,0.997475,0.989426,0.998619,0.97455,0.948903,0.948074,0.982402,0.992977,0.962358,0.998501,0.992188,0.974392,0.949416,0.99203,0.997396,0.999921,0.983586,0.999882,0.992227,0.983546,0.975813,0.998185,0.97017,0.965949,0.998303,0.991201,0.987255,0.978338,0.993647,0.997277,0.982797,0.996449,0.998619,0.995699,0.994949,0.991122,0.985598,0.99057,0.995107,0.973682,0.97893,0.975497,1,0.996409,0.994121,0.975458,0.995147,0.966856,0.993726,0.957702,0.998777,0.963936,0.966027,0.995778,0.988005,0.984138,0.974629,0.997988,0.980232,0.997475,0.97455,0.988636,0.998501,0.988834,0.999605,0.998974,0.97459,0.993608,0.979048,0.990807,0.995068,0.993726,0.9942,0.976207,0.998027,0.994673,0.987098,0.999605,0.993371,0.990846,0.969184,0.993766,0.978969,0.996567,0.902462,0.971985] average_cost 0 f_measure 0.6194816634709341 [0.16,0.827586,0.903226,0.814815,0.866667,0.689655,0.903226,0.666667,0.592593,0.857143,0.8125,0.827586,0.777778,0.416667,0.814815,0.612245,0.903226,0.242424,0.294118,0.093023,0.533333,0.64,0.318182,0.857143,0.4,0.272727,0.25,0.789474,0.64,0.666667,0.83871,0.967742,0.5,0.375,0.385965,0.518519,0.311111,0.222222,0.848485,0.72,0.580645,0.777778,0.705882,0.787879,0.814815,0.32,0.814815,0.75,0.866667,0.648649,0.434783,0.764706,0.740741,0.434783,0.5,0.173913,0.896552,0.8125,0.631579,0.689655,0.72,0.615385,0.551724,0.419355,0.848485,0.307692,0.285714,0.857143,0.580645,0.555556,0.285714,0.692308,0.4,0.774194,0.6875,0.764706,0.774194,0.296296,0.969697,0.9375,0.2,0.8,0.533333,0.666667,0.666667,0.785714,0.896552,0.612245,0.56,0.866667,0.434783,0.827586,0.666667,0.615385,0.648649,0.777778,0.541667,0.758621,0,0.487805] kappa 0.6098484848484849 kb_relative_information_score 795.0826632946843 mean_absolute_error 0.017123437444443085 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] precision 0.6736890653854464 [0.222222,0.923077,0.933333,1,0.928571,0.769231,0.933333,0.6,0.727273,1,0.8125,0.923077,0.7,0.625,1,0.454545,0.933333,0.235294,0.277778,0.074074,0.571429,0.888889,0.25,0.789474,0.555556,0.5,0.1875,0.681818,0.888889,1,0.866667,1,0.75,0.375,0.268293,0.636364,0.241379,0.272727,0.823529,1,0.6,0.7,0.666667,0.764706,1,0.444444,1,0.75,0.928571,0.571429,0.714286,0.722222,0.909091,0.714286,0.416667,0.285714,1,0.8125,0.545455,0.769231,1,0.8,0.615385,0.282609,0.823529,0.222222,0.6,1,0.6,0.5,0.333333,0.9,0.333333,0.8,0.6875,0.722222,0.8,0.363636,0.941176,0.9375,0.5,0.857143,0.413793,0.6,0.647059,0.916667,1,0.454545,0.777778,0.928571,0.714286,0.923077,0.714286,0.8,0.571429,0.7,0.40625,0.846154,0,0.4] predictive_accuracy 0.61375 prior_entropy 6.6438561897747395 recall 0.61375 [0.125,0.75,0.875,0.6875,0.8125,0.625,0.875,0.75,0.5,0.75,0.8125,0.75,0.875,0.3125,0.6875,0.9375,0.875,0.25,0.3125,0.125,0.5,0.5,0.4375,0.9375,0.3125,0.1875,0.375,0.9375,0.5,0.5,0.8125,0.9375,0.375,0.375,0.6875,0.4375,0.4375,0.1875,0.875,0.5625,0.5625,0.875,0.75,0.8125,0.6875,0.25,0.6875,0.75,0.8125,0.75,0.3125,0.8125,0.625,0.3125,0.625,0.125,0.8125,0.8125,0.75,0.625,0.5625,0.5,0.5,0.8125,0.875,0.5,0.1875,0.75,0.5625,0.625,0.25,0.5625,0.5,0.75,0.6875,0.8125,0.75,0.25,1,0.9375,0.125,0.75,0.75,0.75,0.6875,0.6875,0.8125,0.9375,0.4375,0.8125,0.3125,0.75,0.625,0.5,0.75,0.875,0.8125,0.6875,0,0.625] relative_absolute_error 0.8648200729516694 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08850456548158049 root_relative_squared_error 0.8895043513207347 total_cost 0 area_under_roc_curve 0.986449874611894 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[0.984177,1,1,1,0.993671,1,0.90566,0.993671,0.993671,1,1,0.996835,1,0.993671,0.984177,0.993671,1,0.943038,0.968553,0.955696,0.962264,0.981132,0.958861,1,0.987421,0.987342,0.981013,0.993711,1,1,1,1,1,0.977848,0.924528,1,0.974843,0.91195,0.981132,0.987342,0.974684,0.81761,1,1,1,1,1,0.981132,0.987342,1,1,1,1,1,0.993711,0.996835,1,0.996835,0.968553,0.93038,1,0.993711,1,0.842767,0.993671,0.962025,0.933544,1,1,0.984177,0.971519,0.996835,0.987421,1,1,1,0.996835,0.990506,1,0.996835,1,1,0.990506,0.996835,1,1,1,0.974684,1,0.987421,0.955696,1,1,0.990506,0.981013,1,0.981013,1,0.943396,0.974684] area_under_roc_curve 0.9864550991163122 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[0.958861,1,1,1,1,1,1,0.996835,0.993711,1,0.987342,1,0.993711,0.792453,0.993711,0.996835,1,0.987342,1,0.993671,0.996835,0.996835,0.96519,1,0.990506,0.977848,0.955696,1,0.993711,1,0.993711,1,0.993671,0.968354,0.987421,1,0.955696,0.971519,1,1,1,1,1,1,1,1,1,1,1,1,1,0.968553,0.987342,1,0.993671,0.990506,1,1,0.993671,0.987421,0.987342,1,0.993711,0.949686,1,1,0.90566,1,0.984177,0.990506,0.974684,1,1,0.981132,0.892405,0.993711,1,0.993671,1,1,0.981132,1,1,0.984177,0.993711,0.943396,1,0.987421,1,1,1,1,1,0.987421,0.936709,0.987421,1,1,0.987421,0.977848] area_under_roc_curve 0.9861722295199428 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[0.993711,0.996835,1,1,1,1,0.996835,1,0.984177,1,1,1,1,0.974684,1,1,1,0.962264,0.924051,0.943396,1,0.993671,0.930818,0.990506,0.996835,0.981132,0.981132,1,1,1,1,1,1,0.981132,0.977848,1,0.977848,0.955696,1,1,0.949686,1,0.974684,0.981013,1,0.987342,1,0.993671,0.993711,1,0.958861,0.993671,1,0.889241,0.987342,0.930818,1,0.968553,0.987342,0.981013,0.990506,0.977848,1,0.971519,1,1,0.984177,0.974843,0.993671,0.993711,1,0.993711,0.962025,1,1,0.974684,1,1,1,0.996835,1,0.993711,0.993711,1,1,1,1,0.977848,1,0.996835,1,1,0.990506,0.974684,1,0.984177,1,1,0.917722,1] area_under_roc_curve 0.9884635478863147 [0.987421,0.996835,1,1,1,1,1,1,0.996835,1,1,1,1,0.905063,1,0.987421,0.981132,1,0.911392,0.974843,0.946203,0.993671,1,1,0.996835,1,0.993711,1,0.990506,1,1,1,1,0.987421,0.971519,1,0.990506,0.996835,1,1,0.993711,0.996835,0.993671,1,0.996835,1,1,1,1,0.993711,0.987342,0.990506,1,0.990506,0.971519,1,1,1,0.993671,0.971519,0.993671,0.993671,0.984177,0.981013,1,0.937107,0.981013,1,0.977848,0.987421,0.987421,1,0.987342,0.996835,0.974684,0.984177,0.993711,1,1,1,1,0.937107,0.974843,1,0.996835,1,1,0.96519,1,1,0.971519,1,1,0.990506,1,1,0.943396,0.996835,0.892405,0.981132] 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 0.5894844872503355 kappa 0.6146709305539105 kappa 0.5769534333070245 kappa 0.6148533996290596 kappa 0.6272310851366293 kappa 0.6084468127096901 kappa 0.6525035539409256 kappa 0.6081839007820523 kappa 0.6021785460573051 kappa 0.6023354899794855 kb_relative_information_score 79.16726761802556 kb_relative_information_score 79.76533795891557 kb_relative_information_score 78.71436609793244 kb_relative_information_score 77.58134575472128 kb_relative_information_score 81.05115068320602 kb_relative_information_score 78.25876064532464 kb_relative_information_score 81.88682772671967 kb_relative_information_score 78.62110423478799 kb_relative_information_score 79.36692576062623 kb_relative_information_score 80.66957681442534 mean_absolute_error 0.017057431617070955 mean_absolute_error 0.01719992548958305 mean_absolute_error 0.01716816498017337 mean_absolute_error 0.017382663925278697 mean_absolute_error 0.017239041049865362 mean_absolute_error 0.017200043815825416 mean_absolute_error 0.01679411664676012 mean_absolute_error 0.01720978570919321 mean_absolute_error 0.017093175400045107 mean_absolute_error 0.016890025810635523 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 0.59375 predictive_accuracy 0.61875 predictive_accuracy 0.58125 predictive_accuracy 0.61875 predictive_accuracy 0.63125 predictive_accuracy 0.6125 predictive_accuracy 0.65625 predictive_accuracy 0.6125 predictive_accuracy 0.60625 predictive_accuracy 0.60625 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 0.59375 [0,0.5,1,0,0.5,1,1,1,0.5,1,0.5,0.5,0.5,0,1,0,0.5,0,0.5,0,0,1,0,1,1,0.5,0,1,0,1,1,1,0,0.5,0,0,1,0,1,0.5,0,1,1,1,1,0,0,1,1,0.5,0.5,1,1,0.5,0,0,1,1,1,0.5,1,0,0.5,1,0,1,0,1,1,0,0,1,1,0.5,1,1,0,0,1,0.5,0,1,1,1,1,0.5,0.5,1,0,1,0.5,1,1,0.5,1,1,1,1,0,1] recall 0.61875 [0,1,1,0,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,0,0,1,1,0,0,1,1,0,0.5,1,0,1,1,0,0,0,1,0.5,1,1,1,0,0.5,0,1,1,0.5,1,0.5,0.5,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0.5,1,0.5,0,1,0,0,0.5,1,0,0.5,1,0.5,1,0,1,1,0,0.5,1,1,1,0.5,1,1,0,1,0.5,1,0.5,0,0.5,1,1,0.5,0,0] recall 0.58125 [0,1,1,1,0.5,1,0,0.5,0.5,1,1,0.5,1,0,0,1,1,0,0,0,0,0,0,1,0,0.5,0.5,1,1,0.5,1,1,0.5,0.5,1,1,1,0,0,0.5,0,0,1,1,1,0,0.5,0,0.5,1,1,1,1,0,1,0.5,1,1,1,0.5,0,0,0.5,0,1,0.5,0,1,0,0.5,0.5,0.5,1,1,1,1,0.5,0,1,1,0,1,0.5,0.5,1,0,1,1,0.5,0,0,1,1,0.5,1,1,1,0.5,0,0] recall 0.61875 [0,1,0.5,0,0.5,0,0,1,0.5,0.5,1,0.5,1,0,1,1,1,0,0,0,1,1,0.5,1,0,0,0.5,1,0.5,1,1,1,0.5,0,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,1,0.5,1,0,1,1,1,0.5,1,1,1,0,1,0.5,0,0.5,1,0.5,0,0,1,1,1,1,1,0,1,1,0,0.5,0,0.5,0,1,0,1,1,1,0.5,1,0,1,0.5,1,0.5,0.5,0,0.5] recall 0.63125 [0,1,1,1,1,0.5,1,0.5,0,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,1,1,0.5,1,0.5,0,0.5,1,1,0.5,0,1,0,0,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,1,1,1,0,0,0.5,0,0,0.5,1,0.5,1,1,0.5,1,0,1,0.5,1,0,1,0,0.5,0.5,0.5,1,0,0.5,1,1,0,1,1,0,1,1,0.5,0,0,1,1,1,1,0,0.5,1,0,0.5,1,0.5,1,0,1] recall 0.6125 [0.5,1,0.5,1,1,0,1,0.5,1,0.5,1,0.5,0,1,0,1,1,0.5,1,0.5,0.5,0,0.5,1,0,0,0,1,1,0,1,1,0.5,0.5,1,0,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0,1,0.5,1,0,0,1,1,0.5,1,0,0,1,1,1,0,1,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,0,0.5,1,0.5,0,1,1,1,0.5,0.5,0,0.5,1,1,1,1,1,1,0,0.5] recall 0.65625 [0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0,0,0,0.5,0,1,1,0.5,0,0,1,0,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,1,0,0.5,0.5,1,1,0,0.5,0,0.5,1,1,0,1,0.5,1,0.5,0,0.5,0.5,0.5,1,0.5,0.5,1,1,1,0,0,0.5,0.5,1,0,1,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0,1] recall 0.6125 [0,0,1,0.5,1,1,1,1,1,0.5,0,1,1,1,0,1,1,0,0,0,0.5,0.5,0.5,1,0,0,1,0.5,0,0.5,1,1,0,0,1,1,0,0,1,0,0,1,0.5,1,0,0,1,0.5,1,0,0,1,0,0,1,0,1,1,1,1,1,0.5,1,1,1,0,0,0.5,0.5,0.5,1,1,1,1,0,1,1,1,1,1,0,1,0.5,1,0,0.5,1,1,0,1,0,1,0.5,1,1,1,1,1,0,0.5] recall 0.60625 [0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0,0.5,0,0.5,0.5,0,0.5,0.5,0,1,1,0.5,1,1,1,1,0,0.5,0.5,0.5,0,0.5,0,0,1,0,0.5,0.5,0,1,1,1,1,0,0.5,0.5,0,1,0,0.5,0,0.5,0.5,0.5,0.5,0,1,1,1,0.5,0,1,1,0,0,0,1,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,0.5,0,0.5,0.5,1,0.5,1,1,0,1] recall 0.60625 [0,0.5,1,0.5,1,1,1,1,0,1,1,1,1,0,0.5,1,0,1,0,0,0,1,1,1,0,1,1,1,0,0,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0,1,1,0,1,0,0.5,1,0.5,0.5,0.5,0.5,0,1,1,0,0.5,1,0.5,1,0,0,0,0.5,0,0.5,0,1,1,1,1,0,1,1,1,1,0.5,0.5,0,1,0,1,1,0,0,0.5,1,0,0,1] relative_absolute_error 0.8614864453066124 relative_absolute_error 0.868683105534496 relative_absolute_error 0.8670790394026939 relative_absolute_error 0.8779123194585187 relative_absolute_error 0.8706586388820875 relative_absolute_error 0.8686890816073428 relative_absolute_error 0.8481877094323279 relative_absolute_error 0.869181096423898 relative_absolute_error 0.8632916868709636 relative_absolute_error 0.8530316065977523 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 0.08825579221213975 root_mean_squared_error 0.088499243145569 root_mean_squared_error 0.08861712807527247 root_mean_squared_error 0.08940719445616034 root_mean_squared_error 0.08859524388756741 root_mean_squared_error 0.08899587286705299 root_mean_squared_error 0.0875124359530239 root_mean_squared_error 0.08858006842111268 root_mean_squared_error 0.08864418728727025 root_mean_squared_error 0.08792444979107822 root_relative_squared_error 0.8870040858885986 root_relative_squared_error 0.8894508598311642 root_relative_squared_error 0.8906356479531757 root_relative_squared_error 0.8985761138467487 root_relative_squared_error 0.8904157035911776 root_relative_squared_error 0.8944421763338946 root_relative_squared_error 0.8795330743823879 root_relative_squared_error 0.8902631844146657 root_relative_squared_error 0.8909076032662645 root_relative_squared_error 0.8836739692589348 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 1423.7209489992892 usercpu_time_millis 1329.9730539993107 usercpu_time_millis 1188.3947019996413 usercpu_time_millis 1206.6559709992362 usercpu_time_millis 1216.2496759992791 usercpu_time_millis 1185.3537050010345 usercpu_time_millis 1191.005677999783 usercpu_time_millis 1165.5711960002009 usercpu_time_millis 1197.3344620000717 usercpu_time_millis 1204.0426599996863 usercpu_time_millis_testing 145.82307799992122 usercpu_time_millis_testing 137.37568899978214 usercpu_time_millis_testing 140.01831499990658 usercpu_time_millis_testing 139.82967699939763 usercpu_time_millis_testing 137.55166600003577 usercpu_time_millis_testing 133.09378000030847 usercpu_time_millis_testing 140.89060199967207 usercpu_time_millis_testing 132.72616199992626 usercpu_time_millis_testing 151.17171499969118 usercpu_time_millis_testing 134.66974499988282 usercpu_time_millis_training 1277.897870999368 usercpu_time_millis_training 1192.5973649995285 usercpu_time_millis_training 1048.3763869997347 usercpu_time_millis_training 1066.8262939998385 usercpu_time_millis_training 1078.6980099992434 usercpu_time_millis_training 1052.259925000726 usercpu_time_millis_training 1050.115076000111 usercpu_time_millis_training 1032.8450340002746 usercpu_time_millis_training 1046.1627470003805 usercpu_time_millis_training 1069.3729149998035