8706940
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)
6684359
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
20388.3286904169
7708
cache_size
200
7708
class_weight
null
7708
coef0
0.6836226731804709
7708
decision_function_shape
null
7708
degree
2
7708
gamma
3.391119240602175
7708
kernel
"poly"
7708
max_iter
-1
7708
probability
true
7708
random_state
53781
7708
shrinking
false
7708
tol
2.7829431521059083e-05
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
18330748
description
https://api.openml.org/data/download/18330748/description.xml
-1
18330749
predictions
https://api.openml.org/data/download/18330749/predictions.arff
area_under_roc_curve
0.9964745107323231 [0.98836,1,1,0.999842,0.99858,0.994594,0.999803,0.999566,0.997593,0.999961,0.998974,0.999566,0.999684,0.973524,0.999329,0.999566,1,0.991872,0.984178,0.974826,0.998224,0.999566,0.996607,0.999329,0.998698,0.976918,0.992188,0.99929,0.998737,1,0.999724,1,1,0.99053,0.999369,0.998895,0.99345,0.983586,0.999645,0.999842,0.999132,0.998777,1,0.998895,0.996962,0.999921,0.999408,0.99925,0.997514,0.996804,0.994515,0.999053,0.998185,0.992385,0.996962,0.993529,1,0.999408,0.992898,0.996725,0.998698,0.999448,0.997751,0.99929,0.999369,0.993924,0.986742,0.999921,0.997988,0.993766,0.99637,0.999329,0.99637,0.998303,0.989031,0.999448,0.999448,0.991517,0.999961,1,0.995778,0.997475,0.99779,0.995896,0.999211,0.999921,0.999961,0.999092,0.99929,0.999961,0.999487,0.999605,0.998777,0.997869,0.996804,0.997751,0.997869,0.998422,0.967251,0.99783]
average_cost
0
f_measure
0.8248194148090846 [0.648649,0.967742,1,0.933333,0.933333,0.8,1,0.848485,0.83871,0.933333,0.882353,0.875,0.875,0.714286,0.933333,0.933333,1,0.533333,0.411765,0.258065,0.8,0.967742,0.75,1,0.875,0.424242,0.5625,0.848485,0.933333,0.933333,0.9375,0.967742,0.967742,0.580645,0.875,0.875,0.5625,0.545455,0.909091,0.933333,0.742857,0.909091,1,0.848485,0.9375,0.941176,0.827586,0.903226,0.764706,0.83871,0.787879,0.827586,0.866667,0.705882,0.774194,0.529412,1,0.9375,0.702703,0.742857,0.857143,0.848485,0.733333,0.9375,0.909091,0.7,0.5,0.969697,0.83871,0.727273,0.823529,0.933333,0.6875,0.967742,0.756757,0.909091,0.903226,0.685714,1,1,0.75,0.848485,0.83871,0.533333,0.848485,0.967742,0.933333,0.866667,0.933333,0.967742,0.774194,0.903226,0.903226,0.875,0.9375,0.777778,0.777778,0.8125,0.484848,0.903226]
kappa
0.8207070707070707
kb_relative_information_score
986.6037371039492
mean_absolute_error
0.01599826905570106
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
0.8325435879479995 [0.571429,1,1,1,1,0.857143,1,0.823529,0.866667,1,0.833333,0.875,0.875,0.833333,1,1,1,0.571429,0.388889,0.266667,0.857143,1,0.75,1,0.875,0.411765,0.5625,0.823529,1,1,0.9375,1,1,0.6,0.875,0.875,0.5625,0.529412,0.882353,1,0.684211,0.882353,1,0.823529,0.9375,0.888889,0.923077,0.933333,0.722222,0.866667,0.764706,0.923077,0.928571,0.666667,0.8,0.5,1,0.9375,0.619048,0.684211,1,0.823529,0.785714,0.9375,0.882353,0.583333,0.5,0.941176,0.866667,0.705882,0.777778,1,0.6875,1,0.666667,0.882353,0.933333,0.631579,1,1,0.75,0.823529,0.866667,0.571429,0.823529,1,1,0.928571,1,1,0.8,0.933333,0.933333,0.875,0.9375,0.7,0.7,0.8125,0.470588,0.933333]
predictive_accuracy
0.8225
prior_entropy
6.6438561897747395
recall
0.8225 [0.75,0.9375,1,0.875,0.875,0.75,1,0.875,0.8125,0.875,0.9375,0.875,0.875,0.625,0.875,0.875,1,0.5,0.4375,0.25,0.75,0.9375,0.75,1,0.875,0.4375,0.5625,0.875,0.875,0.875,0.9375,0.9375,0.9375,0.5625,0.875,0.875,0.5625,0.5625,0.9375,0.875,0.8125,0.9375,1,0.875,0.9375,1,0.75,0.875,0.8125,0.8125,0.8125,0.75,0.8125,0.75,0.75,0.5625,1,0.9375,0.8125,0.8125,0.75,0.875,0.6875,0.9375,0.9375,0.875,0.5,1,0.8125,0.75,0.875,0.875,0.6875,0.9375,0.875,0.9375,0.875,0.75,1,1,0.75,0.875,0.8125,0.5,0.875,0.9375,0.875,0.8125,0.875,0.9375,0.75,0.875,0.875,0.875,0.9375,0.875,0.875,0.8125,0.5,0.875]
relative_absolute_error
0.8079933866515673
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08228870515552103
root_relative_squared_error
0.8270326045001424
total_cost
0
area_under_roc_curve
0.996522967916567 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.990506,1,0.981132,1,0.993671,0.981013,0.987421,1,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,0.962264,0.996835,1,1,1,1,1,1,0.955696,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.993711,0.993671,0.933544,1]
area_under_roc_curve
0.9963249343205159 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.892405,1,1,1,0.984177,0.993671,0.987421,1,0.993711,1,1,1,0.977848,0.971519,1,1,1,1,1,1,0.996835,0.996835,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.987421,1,0.996835,1,1,1,1,1,1,0.990506,1,1,0.984177,0.996835,1,0.981132,0.996835,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,1,0.974843]
area_under_roc_curve
0.996956104211448 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.936709,1,1,0.993671,1,1,0.939873,0.993671,1,1,1,1,1,1,0.984177,1,1,0.993711,1,1,1,0.996835,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.981013,1,1,1,0.993711,1,0.993671,1,1,1,0.984177,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1]
area_under_roc_curve
0.997276042910596 [1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,1,1,1,0.990506,0.943396,0.974684,1,1,0.996835,1,0.993711,0.990506,1,1,1,1,1,1,1,0.990506,1,1,1,0.962264,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,0.984177,1,1,0.993711,1,1,1,1,1,1,0.996835,1,1,1,0.977848,0.981013,1,0.981132,1,0.993711,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1]
area_under_roc_curve
0.9961308812992599 [1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,0.918239,1,1,1,0.993671,0.987421,0.990506,1,1,1,1,1,0.993671,0.993671,1,1,1,1,1,1,0.977848,1,1,0.993671,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.990506,0.990506,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.939873,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.968354,0.993711,1,1,0.937107,1]
area_under_roc_curve
0.9962430837512937 [0.920886,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,0.955696,1,1,1,1,0.993671,0.990506,0.987342,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.981013,0.996835,1,0.996835,1,1,0.993711,1,1,1,1,0.990506,0.996835,1,1,1,1,0.996835,0.987342,1,1,0.993671,1,0.996835,1,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1]
area_under_roc_curve
0.9956960035029055 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.968354,0.996835,1,0.990506,1,1,0.893082,0.968553,0.993671,1,1,0.996835,1,1,1,1,1,0.974684,0.993671,1,1,1,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.990506,1,1,1,1,0.996835,1,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.974684,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.886076,1]
area_under_roc_curve
0.997515872541995 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.993671,0.996835,0.996835,1,1,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.993671,1,1,1,1,1,1,1,1,1,0.993671,1,0.987421,0.962264,1,0.996835,1,1,0.987342,1,1,0.993671,1,1,1,1,1,1,0.974843,0.937107,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.996835]
area_under_roc_curve
0.9973539129050234 [1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,0.993711,0.990506,0.974843,0.996835,1,0.981132,0.993671,1,0.987421,1,1,1,1,1,1,1,0.993711,1,1,1,0.949367,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.993671,1,1,0.968354,0.990506,1,1,1,1,1,0.996835,1,1,1,1,1,0.987342,1,1,1,1,0.993711,0.987342,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1]
area_under_roc_curve
0.9977091792054775 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.968354,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.993671,1,0.993711,0.993711,0.987342,1,1,1,1,1,0.990506,0.990506,1,0.990506,1,1,1,1,1,0.968553,1,0.993711,1,1,1,0.996835,1,1,0.996835,1,1,0.996835,1,1,0.993711,1,1,1]
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
0.8041924914136829
kappa
0.7978042808624911
kappa
0.8736526236822364
kappa
0.8041924914136829
kappa
0.8167673656359831
kappa
0.8168035375868604
kappa
0.8167963043392427
kappa
0.8167528928557324
kappa
0.86101788605046
kappa
0.7979080323662917
kb_relative_information_score
99.34141196746538
kb_relative_information_score
97.14221705069181
kb_relative_information_score
98.54794532469978
kb_relative_information_score
97.52802897433355
kb_relative_information_score
98.17927869331014
kb_relative_information_score
97.87840986435256
kb_relative_information_score
101.38114562620511
kb_relative_information_score
98.36913884296887
kb_relative_information_score
99.35789622344286
kb_relative_information_score
98.87826453647824
mean_absolute_error
0.015841794678643172
mean_absolute_error
0.016199753365984212
mean_absolute_error
0.01609076536135995
mean_absolute_error
0.016193567998006548
mean_absolute_error
0.01614286573251022
mean_absolute_error
0.016041757760254446
mean_absolute_error
0.015622410590358172
mean_absolute_error
0.016066529331538137
mean_absolute_error
0.015876015711090354
mean_absolute_error
0.0159072300272654
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
0.80625
predictive_accuracy
0.8
predictive_accuracy
0.875
predictive_accuracy
0.80625
predictive_accuracy
0.81875
predictive_accuracy
0.81875
predictive_accuracy
0.81875
predictive_accuracy
0.81875
predictive_accuracy
0.8625
predictive_accuracy
0.8
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
0.80625 [1,0.5,1,0,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1]
recall
0.8 [1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,0,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0]
recall
0.875 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1]
recall
0.80625 [0,1,1,1,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1]
recall
0.81875 [1,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1]
recall
0.81875 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1]
recall
0.81875 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.81875 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5]
recall
0.8625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1]
recall
0.8 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.8000906403355124
relative_absolute_error
0.8181693619183932
relative_absolute_error
0.8126649172404001
relative_absolute_error
0.817856969596289
relative_absolute_error
0.8152962491166764
relative_absolute_error
0.8101897858714353
relative_absolute_error
0.789010635876674
relative_absolute_error
0.8114408753302077
relative_absolute_error
0.8018189753075923
relative_absolute_error
0.8033954559224936
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
0.08169874107845279
root_mean_squared_error
0.08308599133185685
root_mean_squared_error
0.08247599694463648
root_mean_squared_error
0.08310331812990068
root_mean_squared_error
0.08277260131681821
root_mean_squared_error
0.0826395310175451
root_mean_squared_error
0.08059803158756512
root_mean_squared_error
0.0823975528611417
root_mean_squared_error
0.0818717323329692
root_mean_squared_error
0.082212630246582
root_relative_squared_error
0.8211032424291619
root_relative_squared_error
0.8350456320681517
root_relative_squared_error
0.8289149578056285
root_relative_squared_error
0.8352197729406661
root_relative_squared_error
0.831895943907767
root_relative_squared_error
0.8305585370791938
root_relative_squared_error
0.8100406958095937
root_relative_squared_error
0.8281265651026724
root_relative_squared_error
0.822841869954143
root_relative_squared_error
0.8262680228973813
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
1646.5013150009327
usercpu_time_millis
1475.728570992942
usercpu_time_millis
1481.5599110006588
usercpu_time_millis
1516.3852510013385
usercpu_time_millis
1490.6870720005827
usercpu_time_millis
1508.3172520025983
usercpu_time_millis
1487.3479610032518
usercpu_time_millis
1500.4941459992551
usercpu_time_millis
1556.4973130021826
usercpu_time_millis
1482.7168690026156
usercpu_time_millis_testing
141.80146800208604
usercpu_time_millis_testing
143.2879749991116
usercpu_time_millis_testing
137.56412300426746
usercpu_time_millis_testing
141.3222670016694
usercpu_time_millis_testing
139.05088100000285
usercpu_time_millis_testing
137.9753660003189
usercpu_time_millis_testing
138.80567400337895
usercpu_time_millis_testing
146.26715699705528
usercpu_time_millis_testing
146.21803499903763
usercpu_time_millis_testing
138.86329700471833
usercpu_time_millis_training
1504.6998469988466
usercpu_time_millis_training
1332.4405959938304
usercpu_time_millis_training
1343.9957879963913
usercpu_time_millis_training
1375.062983999669
usercpu_time_millis_training
1351.6361910005799
usercpu_time_millis_training
1370.3418860022794
usercpu_time_millis_training
1348.5422869998729
usercpu_time_millis_training
1354.2269890021998
usercpu_time_millis_training
1410.279278003145
usercpu_time_millis_training
1343.8535719978972