5973730
1
Jan van Rijn
9954
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)
4008463
bootstrap
true
6902
class_weight
null
6902
criterion
"gini"
6902
max_depth
null
6902
max_features
0.6073026756626932
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
4
6902
min_samples_split
10
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
34827
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
"mean"
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
false
6948
threshold
0.0
6949
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
12870819
description
https://api.openml.org/data/download/12870819/description.xml
-1
12870820
predictions
https://api.openml.org/data/download/12870820/predictions.arff
area_under_roc_curve
0.9963687657828282 [0.994476,0.999684,1,1,0.999842,0.995896,0.99925,0.999605,0.997554,0.999921,0.998816,0.999961,1,0.99203,0.999961,0.999724,1,0.986506,0.991162,0.98402,0.999566,1,0.997001,1,0.998777,0.970762,0.985204,0.998935,0.998461,1,0.999803,0.999842,0.998935,0.991083,0.999329,0.997869,0.992937,0.985085,0.999171,0.999763,0.99925,0.999487,0.999092,0.998935,0.999132,0.999605,0.998658,0.99929,0.997199,0.996922,0.998658,0.994397,0.996133,0.989031,0.998027,0.983665,0.999961,0.999527,0.992188,0.993963,0.999921,0.999566,0.993529,0.998461,0.998895,0.996607,0.988636,0.999724,0.989741,0.994081,0.996607,0.997672,0.994358,1,0.986427,0.998461,0.999842,0.990294,0.999684,0.999645,0.996133,0.996922,0.998382,0.995936,0.996094,0.997317,1,0.995226,0.999053,1,0.999724,0.999014,0.998777,0.995226,0.997909,0.998382,0.99708,0.995226,0.97459,0.999684]
average_cost
0
f_measure
0.8076742812767379 [0.714286,0.833333,1,1,0.941176,0.740741,0.9375,0.909091,0.823529,0.9375,0.875,0.9375,0.909091,0.758621,0.941176,0.933333,0.941176,0.625,0.588235,0.466667,0.9375,0.967742,0.774194,1,0.848485,0.434783,0.3125,0.857143,0.9375,0.967742,0.9375,0.875,0.83871,0.606061,0.9375,0.8,0.571429,0.606061,0.909091,0.9375,0.823529,0.933333,0.736842,0.742857,0.903226,0.933333,0.8125,0.896552,0.727273,0.8,0.903226,0.628571,0.764706,0.666667,0.8,0.533333,0.969697,0.866667,0.4375,0.538462,0.969697,0.820513,0.727273,0.888889,0.882353,0.789474,0.540541,0.888889,0.56,0.666667,0.740741,0.83871,0.606061,1,0.740741,0.967742,0.969697,0.769231,0.967742,0.933333,0.666667,0.75,0.787879,0.606061,0.705882,0.875,1,0.774194,0.833333,1,0.888889,0.882353,0.9375,0.857143,0.909091,0.8,0.83871,0.709677,0.48,0.909091]
kappa
0.8087121212121212
kb_relative_information_score
1176.209591363868
mean_absolute_error
0.012905324698057698
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
0.8177045294933297 [0.833333,0.75,1,1,0.888889,0.909091,0.9375,0.882353,0.777778,0.9375,0.875,0.9375,0.882353,0.846154,0.888889,1,0.888889,0.625,0.555556,0.5,0.9375,1,0.8,1,0.823529,0.714286,0.3125,0.789474,0.9375,1,0.9375,0.875,0.866667,0.588235,0.9375,0.736842,0.526316,0.588235,0.882353,0.9375,0.777778,1,0.636364,0.684211,0.933333,1,0.8125,1,0.705882,0.857143,0.933333,0.578947,0.722222,0.714286,0.736842,0.571429,0.941176,0.928571,0.4375,0.7,0.941176,0.695652,0.705882,0.8,0.833333,0.681818,0.47619,0.8,0.777778,0.714286,0.909091,0.866667,0.588235,1,0.909091,1,0.941176,1,1,1,0.818182,0.75,0.764706,0.588235,0.666667,0.875,1,0.8,0.75,1,0.8,0.833333,0.9375,1,0.882353,0.736842,0.866667,0.733333,0.666667,0.882353]
predictive_accuracy
0.810625
prior_entropy
6.6438561897747395
recall
0.810625 [0.625,0.9375,1,1,1,0.625,0.9375,0.9375,0.875,0.9375,0.875,0.9375,0.9375,0.6875,1,0.875,1,0.625,0.625,0.4375,0.9375,0.9375,0.75,1,0.875,0.3125,0.3125,0.9375,0.9375,0.9375,0.9375,0.875,0.8125,0.625,0.9375,0.875,0.625,0.625,0.9375,0.9375,0.875,0.875,0.875,0.8125,0.875,0.875,0.8125,0.8125,0.75,0.75,0.875,0.6875,0.8125,0.625,0.875,0.5,1,0.8125,0.4375,0.4375,1,1,0.75,1,0.9375,0.9375,0.625,1,0.4375,0.625,0.625,0.8125,0.625,1,0.625,0.9375,1,0.625,0.9375,0.875,0.5625,0.75,0.8125,0.625,0.75,0.875,1,0.75,0.9375,1,1,0.9375,0.9375,0.75,0.9375,0.875,0.8125,0.6875,0.375,0.9375]
relative_absolute_error
0.6517840756594785
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.07108699820155498
root_relative_squared_error
0.7144512136582629
total_cost
0
area_under_roc_curve
0.9956512220364617 [0.993711,1,1,1,1,1,1,1,0.996835,1,0.993671,1,1,0.996835,1,1,1,0.987342,0.984177,0.993711,1,1,0.993711,1,1,0.996835,0.971519,1,1,1,0.996835,1,0.996835,0.981013,1,0.981132,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,1,0.968354,1,1,1,0.993671,1,0.993711,1,1,0.993711,0.996835,1,1,1,1,1,0.996835,0.993671,1,1,1,0.990506,1,0.996835,1,1,1,1,0.996835,1,1,1,1,1,1,0.984177,0.990506,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.987342,0.901899,1]
area_under_roc_curve
0.9954161193376324 [0.987421,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,1,0.958861,0.993671,0.987421,1,1,1,1,1,0.933544,0.993671,1,1,1,1,1,1,1,1,0.993711,0.974843,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.955696,1,1,1,0.968553,1,1,1,0.971519,1,1,1,0.993671,1,1,1,1,0.968553,1,0.996835,1,1,1,1,1,1,0.981013,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,1,1,0.987342,0.990506,1]
area_under_roc_curve
0.9973947137966723 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.990506,1,0.949367,1,1,0.993671,1,1,0.993671,1,1,1,1,1,1,1,0.981013,1,1,0.993711,0.962264,1,1,0.996835,1,1,1,1,1,0.996835,1,0.990506,1,1,1,1,1,1,1,1,1,0.981132,0.996835,1,1,1,1,1,1,1,1,0.981132,0.993671,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,0.996835,0.943396,1]
area_under_roc_curve
0.9974713398614761 [0.996835,1,1,1,1,0.996835,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.962264,0.990506,1,1,1,1,1,0.993671,0.971519,1,1,1,1,1,1,0.990506,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,0.996835,1,1,0.993711,1,1,1,0.993671,1,0.996835,1,0.96519,1,1,1,1,0.996835,0.987421,1,0.993711,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.987342,0.996835,1,1]
area_under_roc_curve
0.9951379766738315 [1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,0.987421,1,1,1,0.996835,0.993711,0.987342,1,1,1,1,1,0.981013,0.987342,1,1,1,1,1,1,0.987342,1,1,1,0.936709,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.987342,1,0.993671,0.974684,1,0.996835,1,1,1,1,1,1,0.996835,1,0.968553,1,0.993671,0.971519,0.996835,1,1,1,0.949367,1,1,0.962025,1,1,1,1,1,0.993671,0.987421,1,1,1,1,1,1,0.993671,1,1,0.977848,1,0.996835,1,0.993711,1]
area_under_roc_curve
0.9960077322665397 [0.984177,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,0.949367,1,1,1,1,1,1,0.974684,0.993711,1,1,1,1,0.993671,0.993671,0.987421,0.993671,0.977848,0.987342,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,0.993671,0.993711,1,0.996835,0.987421,1,1,0.996835,0.993711,1,0.971519,1,0.990506,0.993671,1,1,0.971519,1,1,1,1,1,0.993711,0.996835,1,0.996835,1,1,1,1,0.996835,1,1,0.996835,0.993711,1,1,1,1,0.981132,1,1]
area_under_roc_curve
0.9980676797229519 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.993671,0.996835,1,0.996835,1,0.993671,0.918239,0.987421,0.996835,1,1,1,1,1,0.987421,1,1,0.993671,1,1,1,1,1,1,1,0.987421,0.993671,1,1,0.993711,1,1,1,1,1,0.993671,1,1,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,0.974843,1,1,1,1,0.993671,1,1,1,1,1,0.996835,1]
area_under_roc_curve
0.9966841811957649 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.987421,0.977848,0.996835,1,1,1,1,1,1,0.987421,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.990506,0.987342,1,0.996835,0.968354,1,1,0.977848,0.981132,1,1,1,1,1,0.949686,0.987421,1,0.996835,0.990506,1,1,1,1,1,1,1,1,1,1,0.993671,0.993711,0.984177,1,0.996835,0.987342,1,0.993711,1,1,1,1,1,0.993711,1,0.996835,0.993671,1,0.990506,1]
area_under_roc_curve
0.996090826765385 [0.987421,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,1,1,0.949686,0.987421,1,1,1,1,1,1,1,1,1,0.996835,0.96519,1,1,1,1,0.996835,0.996835,1,1,1,1,0.993711,1,0.996835,0.993671,1,0.971519,0.996835,0.981132,1,1,1,0.987342,1,1,0.996835,1,1,1,0.990506,1,0.993671,0.981132,1,0.981132,0.974684,1,1,0.987342,1,1,1,1,1,1,1,0.993711,1,1,1,0.990506,1,1,1,1,1,0.987342,1,0.996835,0.993711,1,0.958861,1]
area_under_roc_curve
0.9968827123636652 [0.993711,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.971519,1,1,1,0.993711,0.990506,1,1,1,1,1,1,0.924528,0.987421,1,0.996835,1,1,1,1,1,1,0.993671,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.993711,1,1,0.993671,0.996835,1,0.996835,0.971519,1,0.993711,1,0.996835,1,1,0.993711,1,1,0.984177,1,0.996835,0.996835,1,1,1,0.996835,1,0.974843,1,0.968553,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1]
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
0.7914938988271532
kappa
0.8294377763739735
kappa
0.8420720151610865
kappa
0.7915350600126342
kappa
0.8168035375868604
kappa
0.7788658979624072
kappa
0.854666087437305
kappa
0.7915185974887466
kappa
0.7851840151634812
kappa
0.8041461006910168
kb_relative_information_score
116.66402235394675
kb_relative_information_score
117.01847361590495
kb_relative_information_score
118.26598835550845
kb_relative_information_score
117.85042272201756
kb_relative_information_score
117.40423385744069
kb_relative_information_score
116.64269930345253
kb_relative_information_score
120.2896320064663
kb_relative_information_score
117.70402155923962
kb_relative_information_score
116.8172022306425
kb_relative_information_score
117.55289535924828
mean_absolute_error
0.01295466931678175
mean_absolute_error
0.012888218240248512
mean_absolute_error
0.012915306556057896
mean_absolute_error
0.013040346453141232
mean_absolute_error
0.01281572692674666
mean_absolute_error
0.013086015201268278
mean_absolute_error
0.012468577790819203
mean_absolute_error
0.012818785053754833
mean_absolute_error
0.013082233422630032
mean_absolute_error
0.012983368019128444
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
0.79375
predictive_accuracy
0.83125
predictive_accuracy
0.84375
predictive_accuracy
0.79375
predictive_accuracy
0.81875
predictive_accuracy
0.78125
predictive_accuracy
0.85625
predictive_accuracy
0.79375
predictive_accuracy
0.7875
predictive_accuracy
0.80625
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
0.79375 [1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,1,0,0.5,1,1,1,0,1,0,1,0,1,1,0,1,1,0.5,0,1,0,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0,1]
recall
0.83125 [0,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1]
recall
0.84375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,0,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1]
recall
0.79375 [0.5,0.5,1,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,0,1,1,1,1,0,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,0.5,0,0.5,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5]
recall
0.81875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0,0.5,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,1,1,0,1,1,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1]
recall
0.78125 [0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0,0.5,0,1,1,1,1,0.5,0.5,0,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0,1,0.5,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1]
recall
0.85625 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,0,0,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.79375 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0,0.5,0.5,1,1,0.5,1,1,0,0,1,1,1,1,1,0,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,0,0,0,1,0.5,0.5,1,0,1,1,1,1,1,0,1,0.5,0.5,0,0.5,1]
recall
0.7875 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,0.5,0.5,0.5,1,1,1,0,1,1,0.5,0.5,0.5,0.5,0,1,0,0.5,0,1,1,0.5,1,1,1,0.5,1,0.5,0,1,0,0,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0.5,1]
recall
0.80625 [1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,0,0,1,0,1,0,1,1,1,1,0.5,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1]
relative_absolute_error
0.6542762281202893
relative_absolute_error
0.6509201131438631
relative_absolute_error
0.6522882099019128
relative_absolute_error
0.658603356219253
relative_absolute_error
0.6472589356942747
relative_absolute_error
0.6609098586499119
relative_absolute_error
0.6297261510514739
relative_absolute_error
0.6474133865532733
relative_absolute_error
0.6607188597287884
relative_absolute_error
0.6557256575317386
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
0.07167587067153869
root_mean_squared_error
0.07067949168091493
root_mean_squared_error
0.07063092271740745
root_mean_squared_error
0.07159401281589041
root_mean_squared_error
0.07082843053303409
root_mean_squared_error
0.0722092786029503
root_mean_squared_error
0.06915883371368026
root_mean_squared_error
0.0706641906313268
root_mean_squared_error
0.0717112602886482
root_mean_squared_error
0.07166895268353146
root_relative_squared_error
0.7203696046652504
root_relative_squared_error
0.7103556190261835
root_relative_squared_error
0.7098674825764539
root_relative_squared_error
0.7195469022612248
root_relative_squared_error
0.7118525108115941
root_relative_squared_error
0.7257305560855289
root_relative_squared_error
0.695072431414723
root_relative_squared_error
0.7102018376916912
root_relative_squared_error
0.7207252836998763
root_relative_squared_error
0.7203000762697226
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
3057.0096639999065
usercpu_time_millis
2987.428213999806
usercpu_time_millis
3018.8944730000458
usercpu_time_millis
2957.2664080000095
usercpu_time_millis
3006.361340000012
usercpu_time_millis
2956.931109999914
usercpu_time_millis
2920.760859999973
usercpu_time_millis
2929.6662990000186
usercpu_time_millis
2962.562399000035
usercpu_time_millis
2994.419131999962
usercpu_time_millis_testing
45.94644499991318
usercpu_time_millis_testing
40.95630499989511
usercpu_time_millis_testing
40.15091799999482
usercpu_time_millis_testing
40.42795600003046
usercpu_time_millis_testing
40.04633500005639
usercpu_time_millis_testing
40.03143199997794
usercpu_time_millis_testing
39.98797999997805
usercpu_time_millis_testing
39.944699999978184
usercpu_time_millis_testing
40.88087299999188
usercpu_time_millis_testing
40.58392299998559
usercpu_time_millis_training
3011.0632189999933
usercpu_time_millis_training
2946.4719089999107
usercpu_time_millis_training
2978.743555000051
usercpu_time_millis_training
2916.838451999979
usercpu_time_millis_training
2966.315004999956
usercpu_time_millis_training
2916.899677999936
usercpu_time_millis_training
2880.772879999995
usercpu_time_millis_training
2889.7215990000404
usercpu_time_millis_training
2921.681526000043
usercpu_time_millis_training
2953.835208999976