5974743
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)
4009476
bootstrap
true
6902
class_weight
null
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criterion
"entropy"
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max_depth
null
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max_features
0.16607142256994223
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max_leaf_nodes
null
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min_impurity_split
1e-07
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min_samples_leaf
11
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min_samples_split
19
6902
min_weight_fraction_leaf
0.0
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n_estimators
100
6902
n_jobs
1
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oob_score
false
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random_state
14798
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verbose
0
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warm_start
false
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axis
0
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categorical_features
[]
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copy
true
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fill_empty
0
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missing_values
"NaN"
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strategy
"median"
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strategy_nominal
"most_frequent"
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verbose
0
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categorical_features
[]
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dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
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handle_unknown
"ignore"
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n_values
"auto"
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sparse
false
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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
12872841
description
https://api.openml.org/data/download/12872841/description.xml
-1
12872842
predictions
https://api.openml.org/data/download/12872842/predictions.arff
area_under_roc_curve
0.9905930839959316 [0.99638,1,0.977298,0.995775,0.973666,0.995381,0.992972,0.994946,0.987168,0.999289,0.994749,0.980614,0.999329,0.998144,0.987208,0.999763,0.980614,0.996644,0.999882,0.995341,1,0.964545,0.990998,0.999566,0.868683,0.995183,0.990366,0.935447,1,0.999171,0.992814,0.999921,0.973626,0.999408,0.996684,0.976153,0.995539,0.999368,0.964664,0.998342,0.998105,0.992814,0.988708,0.96415,0.9741,0.999289,0.98322,0.998263,0.994315,0.993288,0.994354,0.994828,0.997434,0.992617,0.987168,0.997355,1,0.966519,0.999013,0.999961,0.999803,0.999566,0.999092,0.999763,0.998342,0.998934,0.999961,0.993288,0.998421,0.972757,0.998539,0.987879,1,0.988234,1,0.999566,0.993091,0.997986,0.99846,0.991077,0.995973,0.989695,0.982825,0.996012,0.989893,0.998302,0.954596,0.999329,0.999645,0.99925,0.9771,0.979193,0.997789,0.999052,0.994709,0.995578,0.999645,0.997394,0.998184,0.999605]
average_cost
0
kappa
0.7271021672046786
kb_relative_information_score
953.5462169565703
mean_absolute_error
0.016171366350369143
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]
predictive_accuracy
0.7298311444652908
prior_entropy
6.6438309601245775
recall
0.7298311444652908 [0.666667,0.9375,0.3125,0.6875,0.75,0.625,0.625,0.8125,0.625,0.875,0.75,0.75,1,0.875,0.5,1,0.4375,0.8125,0.9375,0.9375,1,0.0625,0.5,1,0,0.8125,0.75,0,1,0.9375,0.375,0.9375,0.1875,1,0.8125,0.0625,0.875,1,0.5625,0.8125,0.875,0.25,0.4375,0.375,0.3125,0.9375,0.4375,0.875,0.6875,0.625,0.8125,0.75,0.9375,0.75,0.4375,0.8125,1,0.625,0.9375,1,0.9375,1,0.875,1,0.75,0.875,1,0.6875,0.75,0.1875,0.8125,0.4375,1,0.8125,0.9375,1,0.75,0.9375,0.875,0.625,0.6875,0.6875,0.1875,0.625,0.8125,0.9375,0.1875,1,1,0.9375,0.875,0.125,0.875,0.8125,0.9375,0.875,0.9375,1,0.8125,0.9375]
relative_absolute_error
0.8167359748962911
root_mean_prior_squared_error
0.09949872432040595
root_mean_squared_error
0.08386901472851163
root_relative_squared_error
0.8429154775737274
total_cost
0
area_under_roc_curve
0.9900366212881144 [1,1,0.993711,1,0.955696,0.962025,0.962025,1,1,1,1,1,1,1,0.987342,1,0.996835,1,1,1,1,0.892405,1,0.996835,0.996835,1,1,0.993671,1,0.987421,1,1,1,1,0.987421,0.933544,0.993711,1,0.990506,0.987342,1,1,0.987421,1,1,1,0.949367,1,0.987342,0.993671,1,0.996835,1,1,0.924528,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.996835,1,0.996835,0.898734,1,1,1,0.867089,0.977848,1,0.996835,0.96519,0.996835,1,1,1,1]
area_under_roc_curve
0.9911927294801371 [1,1,1,0.993671,0.996835,0.987342,0.996835,1,1,1,1,1,1,1,1,1,0.974684,1,1,0.996835,1,0.962025,1,1,0.943038,0.974684,0.91195,0.996835,1,1,1,1,1,1,0.981132,0.958861,1,1,0.993671,1,0.996835,0.987421,0.880503,0.841772,1,1,0.981013,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.996835,1,0.949686,1,0.933544,1,0.974684,1,1,1,1,1,0.993671,1,0.993711,1,1,0.955975,1,1,1,0.990506,0.996835,1,1,1,0.996835,1,1,1,0.996835,0.993711,1,1,1]
area_under_roc_curve
0.9911880025475679 [1,1,0.939873,1,1,1,1,1,1,1,1,1,0.993671,0.990506,1,1,1,1,1,1,1,0.977848,0.984177,1,0.962264,1,1,0.962025,1,1,0.990506,1,0.968354,1,1,0.981132,1,1,1,1,1,0.993711,1,1,0.96519,1,0.987342,1,1,0.981132,1,0.971519,1,1,1,0.996835,1,0.924528,0.987421,1,1,1,1,1,1,1,1,0.974843,0.981132,0.977848,1,0.955696,1,1,1,1,1,1,1,0.987342,1,0.949367,0.987421,0.993671,0.993711,1,0.857595,1,0.993671,1,1,0.968354,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.989660208184062 [1,1,0.996835,1,1,1,1,0.996835,1,1,0.993671,1,1,0.993671,0.924051,1,0.977848,1,1,1,1,1,1,1,0.666667,0.993671,1,0.825949,1,1,0.987342,1,0.981013,1,1,1,1,1,0.798742,1,1,0.987421,0.974843,0.993671,1,0.996835,1,0.996835,0.993711,1,1,1,1,0.977848,0.996835,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,1,1,0.993671,1,0.987342,1,1,1,0.996835,0.93038,0.981132,0.990506,1,1,0.981013,0.993711,0.990506,1,1,1,1,1,1,1,0.996835,0.984177,1,1,1,1,1,0.996835,1]
area_under_roc_curve
0.9934514568903745 [0.993671,1,0.962025,1,1,1,1,0.971519,0.880503,0.993711,1,0.962264,1,1,0.968553,1,0.974684,0.990506,1,1,1,0.90566,0.996835,1,0.937107,0.996835,1,0.987421,1,1,1,1,1,1,1,1,1,0.990506,1,0.987421,1,1,0.993671,0.996835,0.981013,1,0.984177,1,0.993711,1,0.993671,0.996835,1,0.981132,0.955696,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,0.993711,1,1,0.993671,0.990506,0.984177,0.987421,0.993711,1,1,0.984177,1,1,1,0.987421,0.987342,1,0.993711,1,1,1,1,0.990506,1]
area_under_roc_curve
0.9919092329432374 [0.993711,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.955975,0.984177,1,1,1,0.984177,0.72327,1,1,0.893082,1,1,1,1,0.987421,1,1,1,1,1,0.996835,1,0.996835,0.955696,1,0.987342,1,1,1,0.993671,0.993711,1,1,0.993671,0.996835,1,0.841772,1,1,1,1,1,1,1,1,1,0.990506,0.996835,1,1,1,1,1,1,1,1,1,1,0.996835,0.977848,0.993711,0.984177,1,0.958861,1,0.996835,0.993711,1,1,1,0.96519,0.996835,0.996835,1,1,1,1,1,1]
area_under_roc_curve
0.990950660775416 [1,1,0.996835,0.962264,1,0.996835,1,1,0.990506,1,0.984177,0.867089,0.993711,1,1,1,1,1,1,1,1,0.974843,0.962264,1,0.946203,1,0.996835,0.968553,1,1,0.993711,1,0.974684,1,0.990506,0.990506,1,1,0.968354,1,1,1,0.987342,1,0.908228,1,1,1,1,1,1,1,1,1,1,0.987421,1,0.952532,1,1,1,1,1,0.993671,1,1,1,1,0.993671,0.981132,1,1,1,0.908228,1,1,0.987421,1,1,1,1,1,0.955696,1,1,1,1,1,1,1,1,0.955975,1,1,1,0.993671,1,0.996835,1,1]
area_under_roc_curve
0.9836255324018788 [0.974843,1,0.933544,1,1,1,0.981132,0.996835,0.96519,1,0.984177,1,1,1,1,1,0.981132,0.990506,1,0.977848,1,1,1,1,0.511076,1,0.987342,0.855346,1,1,0.993711,1,0.886076,1,1,0.968354,1,1,0.882911,1,0.987421,0.990506,1,0.937107,1,1,1,1,1,0.981013,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.955975,1,0.981132,1,1,1,1,1,1,0.993671,0.96519,1,1,0.993671,1,0.984177,0.987421,0.81761,1,1,1,1,1,1,1,1,0.990506,1,0.993671,0.993711,1]
area_under_roc_curve
0.9941614919194329 [0.993711,1,1,1,0.993671,1,0.996835,0.987342,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.981013,1,1,0.971519,1,1,0.977848,1,1,1,0.996835,0.981132,1,1,1,0.974684,1,1,1,1,1,1,0.91195,1,1,1,0.987421,0.971519,1,0.984177,0.993711,0.993711,0.968354,0.987342,0.993711,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.786164,0.996835,0.993671,1,1,1,1,0.996835,1,1,1,1,1,0.971519,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.962264,1,0.996835]
area_under_roc_curve
0.9931574679893329 [1,1,1,0.980892,0.872611,1,0.996815,1,1,1,0.987342,1,1,1,0.996815,1,0.93038,1,1,0.996815,1,0.996815,0.987342,1,0.808917,1,1,0.993631,1,1,1,1,1,1,1,1,1,1,0.977707,1,0.993671,0.980892,1,0.987342,1,0.993671,0.968354,1,1,0.990446,0.977707,1,0.993671,1,0.987342,1,1,1,0.996815,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.993631,1,0.993631,1,1,1,1,1,0.996815,1,0.987342,1,1,1,1,1,1,1,1]
average_cost
0
average_cost
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average_cost
0
average_cost
0
average_cost
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average_cost
0
average_cost
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average_cost
0
average_cost
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average_cost
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kappa
0.7222002998974035
kappa
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kappa
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kappa
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kappa
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kappa
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kappa
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kappa
0.7346390775548887
kappa
0.7158418186123608
kappa
0.7520591763294683
kb_relative_information_score
94.36932801479341
kb_relative_information_score
94.55760319321092
kb_relative_information_score
95.33770849470868
kb_relative_information_score
95.18683832297958
kb_relative_information_score
95.68910975535569
kb_relative_information_score
96.68398592599311
kb_relative_information_score
94.8892745342302
kb_relative_information_score
94.26692272051687
kb_relative_information_score
96.4052837446445
kb_relative_information_score
96.16016225013774
mean_absolute_error
0.016191522105838876
mean_absolute_error
0.01625282206668923
mean_absolute_error
0.016203277326187895
mean_absolute_error
0.016185353183397404
mean_absolute_error
0.016122736947350086
mean_absolute_error
0.01603241908007302
mean_absolute_error
0.016189210434474976
mean_absolute_error
0.01625935329756303
mean_absolute_error
0.016176090081029954
mean_absolute_error
0.01610043566429881
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
0.725
predictive_accuracy
0.75625
predictive_accuracy
0.74375
predictive_accuracy
0.70625
predictive_accuracy
0.74375
predictive_accuracy
0.675
predictive_accuracy
0.7375
predictive_accuracy
0.7375
predictive_accuracy
0.71875
predictive_accuracy
0.7547169811320755
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
0.725 [1,1,0,1,0.5,0.5,0,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,0,1,1,0,1,0,1,1,0,1,0,0,1,1,0.5,0.5,1,1,1,0.5,0,1,0.5,1,0.5,0.5,1,1,1,1,0,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,0,1,1,1,0.5,0,1,1,0.5,0.5,1,1,0.5,1]
recall
0.75625 [1,1,0,0.5,1,0,0.5,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0,1,1,0,0.5,0,0,1,1,0.5,1,1,1,1,0,1,1,0.5,1,0.5,0,0,0.5,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,0,1,0,1,1,1,1,0.5,1,1,1,1,1,0,0.5,1,1,0,1,1,1,1,0.5,1,1,1,1,1,1,1,1]
recall
0.74375 [1,1,0,0.5,0.5,1,1,1,0,1,1,0,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,0,1,1,0,1,1,0,1,0,1,1,0,1,1,1,1,1,1,0,1,0.5,1,0.5,1,0,0,1,0.5,1,1,0,1,1,0,1,1,1,1,1,1,1,0.5,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,0,0.5,1,0.5,0,1,1,1,1,0,1,1,1,1,1,1,1,1]
recall
0.70625 [0.5,1,0.5,1,1,0,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1,0.5,1,1,0,0.5,1,0,0.5,1,0,1,1,0.5,1,0,1,0.5,0,1,1,0,1,1,0,0,0.5,0.5,0.5,1,0.5,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1,0,1,1,0.5,1,0.5,0,0.5,1,1,0,1,1,1,1,0,1,1,1,1,0,0.5,1,1,1,1,1,1,1]
recall
0.74375 [0.5,1,0.5,1,1,1,1,0.5,0,0,1,0,1,1,0,1,0,0.5,1,1,1,0,0,1,0,1,1,0,1,1,1,1,0.5,1,1,1,1,1,1,0,1,0.5,1,0,0.5,1,0.5,1,0,1,0,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0,0.5,0,0,1,1,0.5,1,1,1,0,0.5,1,0,1,1,1,1,0.5,1]
recall
0.675 [0,1,0.5,0,1,1,1,0.5,1,1,1,1,1,1,0,1,0,0.5,1,1,1,0,0,1,0,1,0.5,0,1,1,0,1,0.5,1,1,0,1,1,1,1,1,0,0.5,0,0,1,0.5,0.5,1,1,1,0,0.5,1,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0,0,1,0.5,1,1,1,1,1,0.5,0,0,0,1,0,1,0,1,1,1,1,0,0.5,1,1,1,0.5,1,0.5,1]
recall
0.7375 [1,1,0,0,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0,0,1,0,1,0.5,0,1,1,0,1,0,1,0.5,0,0.5,1,0.5,1,1,0,0,1,0,1,0,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,0.5,1,1,1,0,1,1,0.5,0.5,0,1,1,1,0,1,1,1,1,0.5,1,1,1,0,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1]
recall
0.7375 [0,1,0.5,1,1,1,0,0.5,0.5,0,0,1,1,1,1,1,0,0.5,1,0.5,1,1,1,1,0,1,0.5,0,1,1,0,1,0,1,1,0,1,1,0.5,1,0,0,1,0,0.5,1,0,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,0,1,1,1,1,1,1,1,0,1,1,0.5,0,0.5,1,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1]
recall
0.71875 [0,1,1,1,1,0.5,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,0,0,1,0,1,1,0,0.5,0,1,1,0,0.5,1,0.5,0.5,1,0.5,0,0,0,1,1,1,0.5,0.5,0.5,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0,0.5,0.5,1,1,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,0.5]
recall
0.7547169811320755 [1,0.5,0,0.5,0,1,0.5,1,1,1,0,1,1,1,1,1,0,1,1,1,1,0,0,1,0,1,1,0,1,1,0.5,1,0,1,1,0,1,1,0.5,1,1,0,0.5,0,0,1,0,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,0.5,1,1,1,1,0.5,1,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,0.5,1,1,1,1,1,1]
relative_absolute_error
0.8177524262697292
relative_absolute_error
0.8208483792868665
relative_absolute_error
0.8183461233847324
relative_absolute_error
0.8174408649933399
relative_absolute_error
0.8142784335297099
relative_absolute_error
0.8097199406079522
relative_absolute_error
0.8176387135354881
relative_absolute_error
0.821181290276407
relative_absolute_error
0.8169760679447506
relative_absolute_error
0.8131551102848401
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
0.08406376269249294
root_mean_squared_error
0.08429278458787322
root_mean_squared_error
0.08394707121410591
root_mean_squared_error
0.08385286723901475
root_mean_squared_error
0.08374279912366663
root_mean_squared_error
0.08328950931487623
root_mean_squared_error
0.08392622920938927
root_mean_squared_error
0.08410134314327365
root_mean_squared_error
0.08397371087817183
root_mean_squared_error
0.0834930443256545
root_relative_squared_error
0.8448712019004754
root_relative_squared_error
0.8471729547344434
root_relative_squared_error
0.8436984103617832
root_relative_squared_error
0.8427516263598558
root_relative_squared_error
0.841645402013876
root_relative_squared_error
0.8370927860416602
root_relative_squared_error
0.8434920749174186
root_relative_squared_error
0.845252039791696
root_relative_squared_error
0.8439692846252682
root_relative_squared_error
0.8391383744630367
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
3277.1760379999932
usercpu_time_millis
3214.3301749998727
usercpu_time_millis
3207.2677770000837
usercpu_time_millis
3196.1161110000376
usercpu_time_millis
3206.0296009999547
usercpu_time_millis
3188.1865409999364
usercpu_time_millis
3152.2677160000967
usercpu_time_millis
3198.015193999936
usercpu_time_millis
3208.775750000086
usercpu_time_millis
3160.7308939999257
usercpu_time_millis_testing
36.228143000016644
usercpu_time_millis_testing
40.158825999924375
usercpu_time_millis_testing
36.301518000072974
usercpu_time_millis_testing
36.04091199997583
usercpu_time_millis_testing
36.196858000039356
usercpu_time_millis_testing
36.152554999944186
usercpu_time_millis_testing
36.24680100006117
usercpu_time_millis_testing
36.16315800002212
usercpu_time_millis_testing
36.20589199999813
usercpu_time_millis_testing
35.07347799995841
usercpu_time_millis_training
3240.9478949999766
usercpu_time_millis_training
3174.1713489999483
usercpu_time_millis_training
3170.9662590000107
usercpu_time_millis_training
3160.0751990000617
usercpu_time_millis_training
3169.8327429999154
usercpu_time_millis_training
3152.033985999992
usercpu_time_millis_training
3116.0209150000355
usercpu_time_millis_training
3161.852035999914
usercpu_time_millis_training
3172.569858000088
usercpu_time_millis_training
3125.6574159999673