8808012
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)
6783093
axis
0
7660
categorical_features
[]
7660
copy
true
7660
fill_empty
0
7660
missing_values
"NaN"
7660
strategy
"mean"
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"
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sparse
true
7661
threshold
0.0
7662
copy
true
7663
with_mean
false
7663
with_std
true
7663
C
214.3114381918745
7708
cache_size
200
7708
class_weight
null
7708
coef0
0.524866953041744
7708
decision_function_shape
null
7708
degree
5
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gamma
0.004492543247341406
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kernel
"sigmoid"
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max_iter
-1
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probability
true
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random_state
58226
7708
shrinking
true
7708
tol
0.001266334504706969
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
18531987
description
https://api.openml.org/data/download/18531987/description.xml
-1
18531988
predictions
https://api.openml.org/data/download/18531988/predictions.arff
area_under_roc_curve
0.9965565814393937 [0.989268,0.999961,1,1,0.997909,0.994792,0.999842,0.999961,0.998264,0.999961,0.99858,0.999566,0.999763,0.983783,0.99925,0.999803,1,0.990096,0.983073,0.974669,0.998658,0.999448,0.997475,1,0.998146,0.975537,0.992898,0.999329,0.998737,1,0.999921,1,1,0.989702,0.999724,0.999171,0.993292,0.983546,0.999921,1,0.999369,0.999684,1,0.999171,0.997988,0.999763,0.999329,0.996015,0.997475,0.996409,0.994121,0.99925,0.998382,0.99057,0.99712,0.992266,1,0.999527,0.992622,0.995936,0.99929,0.999329,0.997317,0.999369,0.999448,0.99566,0.987098,1,0.998461,0.994121,0.996133,0.999605,0.996646,0.998185,0.987887,0.999605,0.999605,0.990767,1,1,0.99416,0.998067,0.998027,0.99562,0.99925,0.999724,1,0.999369,0.999605,1,0.999211,0.999527,0.998974,0.99779,0.997396,0.997554,0.997554,0.99854,0.969026,0.998698]
average_cost
0
f_measure
0.8308011940168971 [0.742857,0.909091,1,1,0.933333,0.83871,0.969697,0.967742,0.866667,0.967742,0.8125,0.875,0.909091,0.666667,0.969697,0.933333,1,0.516129,0.368421,0.30303,0.774194,0.967742,0.857143,1,0.866667,0.5,0.516129,0.848485,0.933333,0.896552,0.967742,1,1,0.538462,0.933333,0.882353,0.529412,0.5,0.909091,1,0.848485,0.969697,1,0.875,0.903226,0.969697,0.866667,0.666667,0.764706,0.848485,0.75,0.827586,0.903226,0.727273,0.733333,0.5625,1,0.903226,0.684211,0.75,0.933333,0.882353,0.727273,0.9375,0.909091,0.742857,0.580645,1,0.866667,0.722222,0.787879,0.857143,0.666667,0.967742,0.777778,0.909091,0.9375,0.702703,1,1,0.774194,0.875,0.875,0.482759,0.848485,0.967742,0.933333,0.875,0.967742,0.967742,0.8125,0.785714,0.909091,0.866667,0.9375,0.777778,0.8,0.787879,0.5,0.903226]
kappa
0.8276515151515151
kb_relative_information_score
984.6385001356574
mean_absolute_error
0.016178416422598332
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
0.8380740636212771 [0.684211,0.882353,1,1,1,0.866667,0.941176,1,0.928571,1,0.8125,0.875,0.882353,0.647059,0.941176,1,1,0.533333,0.318182,0.294118,0.8,1,0.789474,1,0.928571,0.5,0.533333,0.823529,1,1,1,1,1,0.7,1,0.833333,0.5,0.45,0.882353,1,0.823529,0.941176,1,0.875,0.933333,0.941176,0.928571,0.714286,0.722222,0.823529,0.75,0.923077,0.933333,0.705882,0.785714,0.5625,1,0.933333,0.590909,0.75,1,0.833333,0.705882,0.9375,0.882353,0.684211,0.6,1,0.928571,0.65,0.764706,1,0.714286,1,0.7,0.882353,0.9375,0.619048,1,1,0.8,0.875,0.875,0.538462,0.823529,1,1,0.875,1,1,0.8125,0.916667,0.882353,0.928571,0.9375,0.7,0.736842,0.764706,0.583333,0.933333]
predictive_accuracy
0.829375
prior_entropy
6.6438561897747395
recall
0.829375 [0.8125,0.9375,1,1,0.875,0.8125,1,0.9375,0.8125,0.9375,0.8125,0.875,0.9375,0.6875,1,0.875,1,0.5,0.4375,0.3125,0.75,0.9375,0.9375,1,0.8125,0.5,0.5,0.875,0.875,0.8125,0.9375,1,1,0.4375,0.875,0.9375,0.5625,0.5625,0.9375,1,0.875,1,1,0.875,0.875,1,0.8125,0.625,0.8125,0.875,0.75,0.75,0.875,0.75,0.6875,0.5625,1,0.875,0.8125,0.75,0.875,0.9375,0.75,0.9375,0.9375,0.8125,0.5625,1,0.8125,0.8125,0.8125,0.75,0.625,0.9375,0.875,0.9375,0.9375,0.8125,1,1,0.75,0.875,0.875,0.4375,0.875,0.9375,0.875,0.875,0.9375,0.9375,0.8125,0.6875,0.9375,0.8125,0.9375,0.875,0.875,0.8125,0.4375,0.875]
relative_absolute_error
0.8170917385150658
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08287184702315177
root_relative_squared_error
0.8328934007864401
total_cost
0
area_under_roc_curve
0.9959694192341374 [0.981132,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.990506,1,0.993711,1,0.981013,0.968354,0.987421,0.993711,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,0.987342,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.977848,1,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,1,1,0.974684,1,1,0.968553,0.996835,1,1,1,1,1,1,0.955696,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.990506,0.987421,1,0.924051,1]
area_under_roc_curve
0.9968777366451714 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.914557,1,1,1,0.981013,0.996835,0.993711,1,1,0.993711,1,1,0.977848,0.971519,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.977848,0.996835,1,0.981132,0.996835,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.974843]
area_under_roc_curve
0.9970352181354984 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.993671,0.993671,1,1,0.990506,1,0.943038,1,1,0.996835,1,1,0.933544,1,1,1,1,1,1,1,0.984177,1,1,0.993711,1,1,1,1,0.993711,1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,0.996835,1,1,0.981132,0.984177,1,1,1,0.993711,1,0.996835,1,1,1,0.984177,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1]
area_under_roc_curve
0.9968404187564687 [1,1,1,1,0.996835,0.993671,1,1,0.996835,1,1,1,1,0.996835,1,1,1,0.987342,0.943396,0.958861,1,1,1,1,0.993711,0.990506,1,1,1,1,1,1,1,0.993671,1,1,1,0.974843,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.981013,1,1,0.993711,1,1,1,1,1,1,0.996835,0.990506,1,1,0.984177,0.974684,1,0.981132,1,0.993711,1,1,0.996835,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.996835,1,1,1]
area_under_roc_curve
0.9964844060982406 [1,1,1,1,1,0.993671,1,1,1,1,0.990506,1,1,0.968553,0.993711,1,1,0.990506,0.987421,0.987342,1,1,1,1,0.996835,0.993671,0.993671,1,1,1,1,1,1,0.974684,1,1,0.990506,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.990506,0.996835,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.946203,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,0.993711,1,1,1,1,1,0.996835,1,1,0.974684,0.993711,1,1,0.962264,1]
area_under_roc_curve
0.9958880662367646 [0.920886,1,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.96519,1,1,1,1,0.993671,0.984177,0.987342,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.968354,1,1,0.996835,1,1,1,1,1,1,0.996835,0.987342,0.996835,1,1,1,1,1,0.990506,1,1,0.993671,1,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.990506,1,1,1,1,1,0.993711,0.993671,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.962264,1]
area_under_roc_curve
0.9956561977549555 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.993671,0.974684,1,1,0.990506,1,1,0.893082,0.974843,0.996835,1,1,0.996835,1,1,0.987421,1,1,0.971519,0.993671,1,1,1,1,1,1,0.974843,0.996835,1,0.987342,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,1,0.996835,1,0.993711,1,1,0.996835,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.968354,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.901899,1]
area_under_roc_curve
0.9977129109943477 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.987342,1,1,1,1,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.987342,0.993671,1,1,1,1,1,1,1,1,1,0.990506,1,0.987421,0.962264,1,0.996835,1,1,0.984177,1,1,0.996835,1,1,1,1,1,1,0.974843,0.949686,1,1,0.996835,1,1,1,1,1,1,1,0.981132,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1]
area_under_roc_curve
0.9972750477668972 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.987342,0.981132,1,1,0.987421,1,1,0.987421,0.993711,1,1,1,1,1,1,0.993711,1,1,1,0.949367,1,1,1,1,1,0.996835,1,1,1,0.993671,0.993711,1,0.990506,1,1,0.952532,0.996835,0.987421,1,1,1,1,1,1,1,1,1,1,0.984177,1,0.996835,1,1,0.993711,0.990506,1,1,1,1,0.993711,1,1,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.9976696222434518 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.968354,0.987421,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,0.996835,1,1,1,0.984177,1,1,1,0.981013,1,0.993711,0.993711,0.993671,1,1,1,1,1,0.993671,0.990506,1,0.993671,1,1,1,1,1,0.968553,1,0.993711,1,1,1,0.993671,1,1,0.996835,1,1,0.990506,1,1,0.993711,0.993671,1,1]
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
0.8168252339031227
kappa
0.842047069973148
kappa
0.8862873613140126
kappa
0.8357678641926569
kappa
0.8168035375868604
kappa
0.7789270064348032
kappa
0.8231066887783305
kappa
0.8293906243829233
kappa
0.8547062539481995
kappa
0.7916008841174613
kb_relative_information_score
98.49178679161237
kb_relative_information_score
97.63983191801736
kb_relative_information_score
98.84271599345618
kb_relative_information_score
97.78011906880037
kb_relative_information_score
97.76046145191899
kb_relative_information_score
97.16265502791246
kb_relative_information_score
100.27799000211533
kb_relative_information_score
98.31131059773662
kb_relative_information_score
99.32848941018706
kb_relative_information_score
99.04313987390101
mean_absolute_error
0.01608239809899581
mean_absolute_error
0.016284027956715397
mean_absolute_error
0.016167661945464297
mean_absolute_error
0.0162768436913663
mean_absolute_error
0.01631281461231198
mean_absolute_error
0.016316785248032234
mean_absolute_error
0.015943743116387625
mean_absolute_error
0.01616655210831063
mean_absolute_error
0.016101639915445314
mean_absolute_error
0.0161316975329536
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
0.81875
predictive_accuracy
0.84375
predictive_accuracy
0.8875
predictive_accuracy
0.8375
predictive_accuracy
0.81875
predictive_accuracy
0.78125
predictive_accuracy
0.825
predictive_accuracy
0.83125
predictive_accuracy
0.85625
predictive_accuracy
0.79375
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
0.81875 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,0.5,0,1,0,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1]
recall
0.84375 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,0,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0.5,0]
recall
0.8875 [1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,1]
recall
0.8375 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5]
recall
0.81875 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,0,0,1,1,0.5,1,1,0,0,0.5,1,0,0.5,1,0.5,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1]
recall
0.78125 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,0.5,1,0,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0,1,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0,1]
recall
0.825 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,0,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0,0,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.83125 [0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1]
recall
0.85625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1]
recall
0.79375 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0.5,0,0,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.8122423282321103
relative_absolute_error
0.8224256543795642
relative_absolute_error
0.816548583104256
relative_absolute_error
0.8220628126952663
relative_absolute_error
0.8238795258743411
relative_absolute_error
0.8240800630319296
relative_absolute_error
0.8052395513327071
relative_absolute_error
0.8164925307227577
relative_absolute_error
0.8132141371437014
relative_absolute_error
0.8147321986340188
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
0.08250345289576638
root_mean_squared_error
0.08327429300321681
root_mean_squared_error
0.08274533479545768
root_mean_squared_error
0.08333939423386788
root_mean_squared_error
0.08350336985491416
root_mean_squared_error
0.08358620587122312
root_mean_squared_error
0.0817624678249983
root_mean_squared_error
0.08281098309209198
root_mean_squared_error
0.08243829731656735
root_mean_squared_error
0.08273728358277412
root_relative_squared_error
0.829190900497023
root_relative_squared_error
0.8369381350720846
root_relative_squared_error
0.831621905057188
root_relative_squared_error
0.8375924270583269
root_relative_squared_error
0.8392404440576483
root_relative_squared_error
0.8400729773462075
root_relative_squared_error
0.8217437203303786
root_relative_squared_error
0.8322816952634361
root_relative_squared_error
0.8285360622873216
root_relative_squared_error
0.8315409873251312
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
1675.756167000145
usercpu_time_millis
1507.8462889996445
usercpu_time_millis
1471.8534170006023
usercpu_time_millis
1503.7993140012986
usercpu_time_millis
1469.6238540000195
usercpu_time_millis
1513.479629998983
usercpu_time_millis
1478.0662640005175
usercpu_time_millis
1492.1937459985202
usercpu_time_millis
1490.282450999075
usercpu_time_millis
1479.7354679994896
usercpu_time_millis_testing
151.57276900026773
usercpu_time_millis_testing
159.50439799962624
usercpu_time_millis_testing
151.2084700007108
usercpu_time_millis_testing
158.73798500069825
usercpu_time_millis_testing
150.46259500013548
usercpu_time_millis_testing
159.70380699945963
usercpu_time_millis_testing
150.34590499999467
usercpu_time_millis_testing
151.64983999966353
usercpu_time_millis_testing
150.40985099949467
usercpu_time_millis_testing
151.81584899983136
usercpu_time_millis_training
1524.1833979998773
usercpu_time_millis_training
1348.3418910000182
usercpu_time_millis_training
1320.6449469998915
usercpu_time_millis_training
1345.0613290006004
usercpu_time_millis_training
1319.161258999884
usercpu_time_millis_training
1353.7758229995234
usercpu_time_millis_training
1327.7203590005229
usercpu_time_millis_training
1340.5439059988566
usercpu_time_millis_training
1339.8725999995804
usercpu_time_millis_training
1327.9196189996583