6683126 1 Jan van Rijn 9954 Supervised Classification 7096 sklearn.model_selection._search.RandomizedSearchCV(estimator=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) 4715460 bootstrap false 6902 class_weight null 6902 criterion "entropy" 6902 max_depth null 6902 max_features "auto" 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 1 6902 min_samples_split 2 6902 min_weight_fraction_leaf 0.0 6902 n_estimators 10 6902 n_jobs 1 6902 oob_score false 6902 random_state 1 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 true 6948 threshold 0.0 6949 cv null 7096 error_score "raise" 7096 fit_params {} 7096 iid true 7096 n_iter 50 7096 n_jobs -1 7096 param_distributions {"classifier__min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__max_features": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], "classifier__min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__criterion": ["gini", "entropy"], "imputation__strategy": ["mean", "median", "most_frequent"]} 7096 pre_dispatch "2*n_jobs" 7096 random_state 1 7096 refit true 7096 return_train_score true 7096 scoring null 7096 verbose 0 7096 openml-python Sklearn_0.18.1. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 14297048 description https://api.openml.org/data/download/14297048/description.xml -1 14297049 predictions https://api.openml.org/data/download/14297049/predictions.arff -1 14297050 trace https://api.openml.org/data/download/14297050/trace.arff area_under_roc_curve 0.9826929450757578 [0.967073,0.999053,1,1,0.999566,0.991694,0.999803,0.995936,0.992858,0.967487,0.997356,0.99931,0.990826,0.956499,0.997376,0.961628,1,0.938901,0.984513,0.940893,0.997652,1,0.995995,1,0.997909,0.865964,0.977391,0.994062,0.987019,0.999487,0.999961,0.999882,0.963936,0.955611,0.999566,0.965534,0.959122,0.982777,0.999211,0.998994,0.992779,0.995245,0.999684,0.992128,0.99929,0.962753,0.9651,0.998067,0.953559,0.961707,0.984592,0.925584,0.988459,0.911103,0.987038,0.942748,1,0.999369,0.99349,0.985934,0.999467,0.996173,0.994062,0.997159,0.996113,0.955769,0.981218,0.999645,0.948844,0.933475,0.954723,0.998895,0.988104,0.996903,0.948509,0.997672,0.999132,0.974294,0.999684,0.998875,0.993134,0.964627,0.99491,0.993056,0.988518,0.999014,0.999842,0.958688,0.994673,0.999842,0.999901,0.998875,0.99491,0.95784,0.997455,0.997416,0.9927,0.987354,0.944898,0.99345] average_cost 0 f_measure 0.7089016579963584 [0.571429,0.764706,0.969697,1,0.8,0.592593,0.9375,0.8125,0.645161,0.967742,0.75,0.882353,0.647059,0.709677,0.875,0.903226,0.914286,0.387097,0.470588,0.153846,0.764706,0.933333,0.733333,0.969697,0.810811,0.24,0.30303,0.645161,0.592593,0.896552,0.909091,1,0.882353,0.424242,0.810811,0.777778,0.545455,0.529412,0.848485,0.848485,0.645161,0.8125,0.857143,0.6875,0.83871,0.764706,0.875,0.827586,0.555556,0.705882,0.484848,0.606061,0.709677,0.428571,0.551724,0.307692,1,0.848485,0.571429,0.5,0.909091,0.789474,0.666667,0.645161,0.702703,0.411765,0.444444,0.941176,0.551724,0.444444,0.5,0.875,0.571429,0.875,0.666667,0.967742,0.823529,0.428571,0.914286,0.896552,0.588235,0.705882,0.645161,0.705882,0.484848,0.848485,0.969697,0.611111,0.764706,0.969697,0.941176,0.823529,0.62069,0.615385,0.702703,0.875,0.727273,0.571429,0.083333,0.8125] kappa 0.7133838383838385 kb_relative_information_score 1165.5623762659272 mean_absolute_error 0.012290037587412506 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 0.7115481827683836 [0.666667,0.722222,0.941176,1,0.736842,0.727273,0.9375,0.8125,0.666667,1,0.75,0.833333,0.611111,0.733333,0.875,0.933333,0.842105,0.4,0.444444,0.2,0.722222,1,0.785714,0.941176,0.714286,0.333333,0.294118,0.666667,0.727273,1,0.882353,1,0.833333,0.411765,0.714286,0.7,0.529412,0.5,0.823529,0.823529,0.666667,0.8125,0.789474,0.6875,0.866667,0.722222,0.875,0.923077,0.5,0.666667,0.470588,0.588235,0.733333,0.5,0.615385,0.4,1,0.823529,0.666667,0.583333,0.882353,0.681818,0.714286,0.666667,0.619048,0.388889,0.4,0.888889,0.615385,0.545455,0.583333,0.875,0.526316,0.875,0.818182,1,0.777778,0.5,0.842105,1,0.555556,0.666667,0.666667,0.666667,0.470588,0.823529,0.941176,0.55,0.722222,0.941176,0.888889,0.777778,0.692308,0.8,0.619048,0.875,0.705882,0.526316,0.125,0.8125] predictive_accuracy 0.71625 prior_entropy 6.6438561897747395 recall 0.71625 [0.5,0.8125,1,1,0.875,0.5,0.9375,0.8125,0.625,0.9375,0.75,0.9375,0.6875,0.6875,0.875,0.875,1,0.375,0.5,0.125,0.8125,0.875,0.6875,1,0.9375,0.1875,0.3125,0.625,0.5,0.8125,0.9375,1,0.9375,0.4375,0.9375,0.875,0.5625,0.5625,0.875,0.875,0.625,0.8125,0.9375,0.6875,0.8125,0.8125,0.875,0.75,0.625,0.75,0.5,0.625,0.6875,0.375,0.5,0.25,1,0.875,0.5,0.4375,0.9375,0.9375,0.625,0.625,0.8125,0.4375,0.5,1,0.5,0.375,0.4375,0.875,0.625,0.875,0.5625,0.9375,0.875,0.375,1,0.8125,0.625,0.75,0.625,0.75,0.5,0.875,1,0.6875,0.8125,1,1,0.875,0.5625,0.5,0.8125,0.875,0.75,0.625,0.0625,0.8125] relative_absolute_error 0.6207089690612365 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.07167108478439961 root_relative_squared_error 0.7203215046897068 total_cost 0 area_under_roc_curve 0.9785276600191068 [0.987421,1,1,1,1,1,1,1,0.990506,1,0.987342,1,1,1,1,1,1,0.691456,0.97943,0.984277,1,1,0.981132,1,1,0.990506,0.974684,1,1,1,1,1,0.996835,0.987342,1,0.993711,1,1,1,1,1,1,1,0.992089,1,1,1,1,1,0.716772,0.960443,0.996835,1,0.97943,0.987421,0.981132,1,1,0.981132,1,1,1,1,1,1,0.71519,0.988924,1,1,0.937107,0.705696,1,1,1,1,1,0.996835,0.958861,1,1,1,0.996835,0.987421,1,0.981013,0.996835,1,1,0.984177,1,1,1,1,0.971519,1,0.981013,1,0.984177,0.867089,1] area_under_roc_curve 0.9868795279038294 [0.943396,1,1,1,1,0.993711,1,1,1,1,1,0.996835,0.984177,0.981013,1,1,1,0.969937,0.996835,0.993711,1,1,1,1,1,0.962025,0.993671,1,1,1,1,1,1,0.996835,1,0.993711,0.993711,1,1,1,1,1,1,0.990506,1,1,1,1,0.702532,1,1,0.713608,1,1,0.993711,0.918239,1,1,1,0.939873,1,1,1,0.993671,1,0.987342,1,1,0.924528,0.993711,0.984177,1,1,1,1,0.987342,1,0.993671,1,0.995253,0.996835,0.996835,0.987421,0.949686,0.996835,1,1,0.97943,1,1,1,1,0.996835,0.990506,0.987342,1,1,0.993671,1,1] area_under_roc_curve 0.9865609575272669 [0.97943,1,1,1,1,0.984177,1,0.984177,0.969937,1,1,1,1,1,0.984177,1,1,0.969937,0.977987,0.686709,0.974843,1,0.995253,1,1,0.974684,0.996835,0.993711,0.996835,1,1,1,1,0.969937,1,1,0.987421,0.95283,1,1,0.982595,1,1,1,1,1,1,1,0.955696,1,0.985759,1,1,0.996835,1,0.988924,1,1,0.987421,0.950949,1,0.993711,0.996835,1,0.996835,0.973101,0.984177,1,0.962264,0.892405,0.957278,1,0.987421,0.996835,1,1,1,0.954114,0.996835,1,1,0.996835,0.973101,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,1,1,0.993671,0.921384,1] area_under_roc_curve 0.9879665980017511 [0.987342,0.996835,1,1,1,0.946203,1,1,0.987342,1,1,0.996835,1,1,1,1,1,0.987342,0.893082,0.971519,1,1,1,1,1,0.968354,0.981013,1,1,1,1,1,0.996835,0.990506,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.984177,1,0.90566,0.686709,1,0.977848,1,1,1,0.981013,1,1,1,1,0.990506,0.993671,0.901899,1,0.993711,0.968354,0.993671,1,0.993711,1,1,1,1,0.933544,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.993671,1,0.987421,1,0.993671,0.943396,0.97943] area_under_roc_curve 0.9640570267096571 [1,1,1,1,1,0.993671,1,1,1,1,1,1,0.996855,0.440252,1,1,1,1,1,0.990506,0.993671,1,1,1,1,0.71519,0.974684,1,0.987421,1,1,1,0.732595,0.723101,1,1,0.987342,0.974684,1,1,0.990506,1,1,1,1,1,1,1,1,0.996835,1,0.443396,0.981013,0.937107,0.97943,0.683544,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.984177,0.707278,0.996835,1,1,0.993711,0.718354,1,1,0.962025,1,1,1,1,0.993671,0.988924,0.974843,1,1,1,1,1,1,0.996835,1,0.459119,0.996835,1,0.996835,0.993711,0.91195,1] area_under_roc_curve 0.982585607236685 [0.939873,1,1,1,1,0.993671,1,0.973101,1,0.743671,0.996835,1,0.930818,1,1,1,1,0.996835,1,0.946203,1,1,0.990506,1,1,0.984177,0.968354,0.993711,1,1,1,1,0.996835,0.987342,1,0.72943,0.987342,0.996835,1,1,0.974684,0.993671,1,1,1,1,1,0.996835,1,0.990506,1,1,0.996835,1,0.993671,0.996835,1,0.996835,0.984177,1,1,1,0.987421,1,1,0.996835,0.993711,1,0.993671,1,0.990506,0.993671,1,1,0.962025,0.993711,0.996835,0.990506,1,1,0.981132,0.734177,1,0.990506,0.993711,1,1,0.993711,0.993671,1,1,1,0.968553,1,0.996835,1,0.974684,0.968553,0.930818,0.969937] area_under_roc_curve 0.9838119974524322 [1,0.996855,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,0.990506,1,0.987421,1,0.966772,0.996835,1,1,1,0.993671,0.433962,0.924528,0.982595,0.987421,0.996835,1,1,1,0.996855,1,1,1,1,1,1,0.993671,0.974684,1,1,1,0.993671,0.721519,0.995253,0.993711,0.993711,0.981132,1,0.987342,1,0.968354,0.971519,1,1,0.996835,1,0.996835,1,1,0.992089,1,0.981132,1,1,0.996835,0.957278,0.990566,1,0.960443,1,1,1,1,1,1,1,0.987342,1,1,1,0.976266,0.998418,1,0.471698,1,1,1,1,0.981013,0.993711,1,1,1,1,0.977848,0.993671] area_under_roc_curve 0.9822603196401559 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.71519,1,0.899371,0.996835,0.949367,1,1,1,1,1,0.993711,0.924528,1,0.889937,1,1,1,1,1,1,1,0.721519,0.949367,1,1,1,1,1,0.992089,1,1,1,1,0.993711,1,0.974843,0.996835,0.993671,1,1,0.993671,1,1,1,1,1,0.966772,1,1,1,0.968553,0.974843,1,0.710443,0.966772,0.993711,0.996835,1,0.971698,1,1,1,0.993711,1,1,0.982595,1,1,1,0.996835,1,1,0.993711,0.993711,1,1,1,1,0.993711,1,1,0.984177,0.91195,0.938291,1] area_under_roc_curve 0.9852335602260964 [0.981132,1,1,1,1,0.993711,1,1,0.996835,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.427673,1,1,1,1,1,1,1,1,1,1,0.996835,0.987342,1,1,0.974843,1,1,0.993671,0.995253,0.71519,1,0.993671,0.981132,1,0.981013,1,1,0.713608,0.973101,0.987421,1,1,0.985759,1,0.996835,1,0.985759,1,1,1,0.993671,1,0.984177,1,1,0.993711,0.974684,1,1,0.996835,1,1,1,1,0.984177,0.981132,1,1,0.996835,1,1,0.993671,1,0.996835,1,0.998418,1,1,1,1,0.993711,0.996835,0.939873,1] area_under_roc_curve 0.99210577382374 [0.789308,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.974684,1,1,1,0.974843,0.952532,1,0.996835,1,1,1,1,0.839623,1,0.996835,0.974684,1,1,1,1,1,1,0.990506,0.993671,1,1,1,1,0.996835,1,0.96519,1,1,1,0.996835,1,0.993711,0.993671,0.996835,1,0.936709,1,1,1,1,1,1,1,1,0.974684,1,0.993711,1,0.984177,1,0.981013,1,1,1,0.990506,1,0.933544,0.993671,1,1,1,0.996835,1,1,1,0.987421,0.993671,0.996835,1,0.984177,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1] 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 0.7157408504086226 kappa 0.7599115463591849 kappa 0.6778904985591916 kappa 0.7157408504086226 kappa 0.7093666087505923 kappa 0.6842188363464119 kappa 0.6904611497157296 kappa 0.7347228801515869 kappa 0.7283209603538146 kappa 0.7157745144481289 kb_relative_information_score 118.88701812405363 kb_relative_information_score 120.45742804220363 kb_relative_information_score 114.14255289640045 kb_relative_information_score 111.09019987467482 kb_relative_information_score 117.87500027032593 kb_relative_information_score 113.58115301584327 kb_relative_information_score 114.78049737997708 kb_relative_information_score 120.75786471312529 kb_relative_information_score 121.06596814408935 kb_relative_information_score 112.92469380523646 mean_absolute_error 0.011530565476190172 mean_absolute_error 0.01162488095238106 mean_absolute_error 0.012702708333333618 mean_absolute_error 0.013873034604283605 mean_absolute_error 0.011452589285714096 mean_absolute_error 0.012955029761904565 mean_absolute_error 0.012651458333333688 mean_absolute_error 0.011078005952381387 mean_absolute_error 0.01164315476190489 mean_absolute_error 0.01338894841269803 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 0.71875 predictive_accuracy 0.7625 predictive_accuracy 0.68125 predictive_accuracy 0.71875 predictive_accuracy 0.7125 predictive_accuracy 0.6875 predictive_accuracy 0.69375 predictive_accuracy 0.7375 predictive_accuracy 0.73125 predictive_accuracy 0.71875 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 0.71875 [1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0.5,0,1,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,0.5,0.5,1,0,0,0,1,1,0,0.5,1,1,0.5,1,1,0,0.5,1,1,0,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,0.5,0,1] recall 0.7625 [0,0.5,1,1,1,0,0.5,1,1,1,1,1,0.5,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0,0,0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,1,1,1,1,0.5,0,0.5,1,1,0.5,0.5,0] recall 0.68125 [0,0.5,1,1,1,0.5,1,0.5,0,1,1,1,1,1,0.5,1,1,0,0,0,0,1,0,1,1,0.5,0.5,0,0.5,1,1,1,1,0.5,1,1,0,0,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,0,1,1,0,0,1,1,0.5,0,0.5,0,0.5,1,0,0,0.5,1,1,1,0,1,1,0,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1] recall 0.71875 [0.5,1,1,1,0.5,0,1,1,0.5,1,0.5,0.5,0,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,0,1,0.5,1,1,1,0.5,1,1,1,1,0.5,0.5,0,1,0,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0.5,1,0,1,0.5,0,0.5] recall 0.7125 [0.5,1,1,1,1,0.5,1,1,0,1,1,1,0,0,1,1,1,1,1,0,0.5,1,1,1,1,0,0.5,1,0,0.5,1,1,0.5,0,1,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0.5,1,0.5,0,0,0.5,0,0,0,1,1,1,0,1,1,1,1,0.5,0.5,0,1,0.5,0.5,0.5,1,1,0,0.5,1,1,0,1,1,1,1,0.5,0.5,0,1,1,1,1,1,1,0.5,1,0,1,1,1,1,0,1] recall 0.6875 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,0,1,1,1,1,0.5,1,0,0.5,0.5,0.5,1,1,0,0,0,0,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,0.5,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0,0,1,0.5,1,0.5,1,1,1,0,0.5,1,1,0,1,1,1,0.5,1,1,1,0,1,0.5,1,0.5,0,0,0.5] recall 0.69375 [1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0,0,0.5,0,0,1,1,1,0,1,1,0.5,0.5,1,1,0.5,0.5,1,1,0,0.5,0,0.5,1,0,0,1,0,0,0.5,0,1,0.5,0,1,0.5,1,1,0,1,0,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,0,1,0.5,1,0.5,1,1,0,1,1,1,1,0.5,1,1,1,0.5,0,0,1] recall 0.7375 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,0,0.5,1,0,0.5,0,1,0.5,1,1,0.5,0,0,1,0,1,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,0,0.5,0.5,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,0,0,1,0.5,0,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1] recall 0.73125 [0,1,1,1,0.5,0,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0,0,1,0.5,1,0.5,0,1,0.5,0,1,0.5,1,1,0.5,0.5,0,1,1,0,0.5,1,1,0.5,0,1,1,0.5,1,0.5,1,1,1,0,1,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0.5,0,0,1,1,0,1,0,1] recall 0.71875 [0,0.5,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,0,1,1,0,1,0.5,0,1,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0,0.5,1,0,0.5,1,1,1,1,0,1,1,0,1,1,1,0.5,1,0.5,1,0,1,0,1,0,1,1,0,1,0.5,1,0,1,0,1,0.5,1,0.5,1,1,1,1,0.5,0.5,0,1,1,1,0,1] relative_absolute_error 0.5823517917267753 relative_absolute_error 0.5871151996152041 relative_absolute_error 0.6415509259259393 relative_absolute_error 0.7006583133476556 relative_absolute_error 0.5784136002885898 relative_absolute_error 0.6542944324194214 relative_absolute_error 0.6389625420875589 relative_absolute_error 0.5594952501202711 relative_absolute_error 0.5880381192881248 relative_absolute_error 0.6762095157928286 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 0.06960970593756322 root_mean_squared_error 0.0687304880062415 root_mean_squared_error 0.07372717396289659 root_mean_squared_error 0.07610321352169905 root_mean_squared_error 0.06954141558914925 root_mean_squared_error 0.07411791962290817 root_mean_squared_error 0.07320827878958387 root_mean_squared_error 0.06714960457695793 root_mean_squared_error 0.06908531775663573 root_mean_squared_error 0.0748323486967372 root_relative_squared_error 0.699603867763247 root_relative_squared_error 0.6907673950749239 root_relative_squared_error 0.740985978449054 root_relative_squared_error 0.7648660745205369 root_relative_squared_error 0.6989175239375143 root_relative_squared_error 0.7449131200936547 root_relative_squared_error 0.7357708857357065 root_relative_squared_error 0.6748789187954575 root_relative_squared_error 0.6943335682461759 root_relative_squared_error 0.7520934024488426 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