8959040
1935
Hilde Weerts
9954
Supervised Classification
8317
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1)
6894644
categorical_features
[]
7644
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
7644
handle_unknown
"ignore"
7644
n_values
"auto"
7644
sparse
true
7644
threshold
0.0
7645
copy
true
7646
with_mean
false
7646
with_std
true
7646
C
5.73796979652682
7650
cache_size
200
7650
class_weight
null
7650
coef0
0.37602701870407707
7650
decision_function_shape
"ovr"
7650
degree
3
7650
gamma
0.0033271124361024993
7650
kernel
"rbf"
7650
max_iter
-1
7650
probability
false
7650
random_state
1
7650
shrinking
false
7650
tol
0.000319233217278817
7650
verbose
false
7650
axis
0
8316
categorical_features
[]
8316
copy
true
8316
fill_empty
0
8316
missing_values
"NaN"
8316
strategy
"mean"
8316
strategy_nominal
"most_frequent"
8316
verbose
0
8316
memory
null
8317
openml-python
Sklearn_0.19.1.
study_98
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
18833334
description
https://api.openml.org/data/download/18833334/description.xml
-1
18833335
predictions
https://api.openml.org/data/download/18833335/predictions.arff
area_under_roc_curve
0.9179292929292928 [0.904356,0.968434,1,1,0.9375,0.874684,1,1,0.936869,0.96875,0.874684,0.999684,1,0.811869,1,0.90625,1,0.745265,0.716856,0.558712,0.905934,0.96875,0.966856,1,0.968119,0.715278,0.747475,0.967803,0.937184,1,0.999684,1,0.999369,0.810606,0.905619,0.967487,0.903093,0.715593,0.967487,0.999684,0.842487,0.968119,1,0.999053,0.968434,0.96875,0.874684,0.968119,0.904672,0.905934,0.936553,0.905619,0.90625,0.873737,0.811553,0.716856,1,0.968434,0.84154,0.904672,0.999369,0.968434,0.873422,0.999053,0.968119,0.842803,0.653409,1,0.874369,0.842487,0.905934,0.875,0.93529,0.96875,0.905303,0.968434,0.968434,0.81029,0.96875,0.999684,0.9375,0.905619,0.90625,0.873106,0.936869,0.937184,0.96875,0.936237,0.937184,1,0.874369,0.968119,0.968434,0.968119,0.96875,0.936869,0.936553,0.96654,0.686553,0.937184]
average_cost
0
f_measure
0.8373317243362379 [0.742857,0.9375,1,1,0.933333,0.827586,1,1,0.875,0.967742,0.827586,0.969697,1,0.714286,1,0.896552,1,0.410256,0.482759,0.133333,0.866667,0.967742,0.810811,1,0.909091,0.411765,0.5,0.882353,0.903226,1,0.969697,1,0.941176,0.625,0.83871,0.857143,0.666667,0.424242,0.857143,0.969697,0.709677,0.909091,1,0.914286,0.9375,0.967742,0.827586,0.909091,0.764706,0.866667,0.848485,0.83871,0.896552,0.75,0.689655,0.482759,1,0.9375,0.647059,0.764706,0.941176,0.9375,0.727273,0.914286,0.909091,0.733333,0.333333,1,0.8,0.709677,0.866667,0.857143,0.756757,0.967742,0.8125,0.9375,0.9375,0.606061,0.967742,0.969697,0.933333,0.83871,0.896552,0.705882,0.875,0.903226,0.967742,0.823529,0.903226,1,0.8,0.909091,0.9375,0.909091,0.967742,0.875,0.848485,0.789474,0.48,0.903226]
kappa
0.8358585858585859
kb_relative_information_score
1339.4325752976727
mean_absolute_error
0.0032499999999999855
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
0.8441104606906532 [0.684211,0.9375,1,1,1,0.923077,1,1,0.875,1,0.923077,0.941176,1,0.833333,1,1,1,0.347826,0.538462,0.142857,0.928571,1,0.714286,1,0.882353,0.388889,0.5,0.833333,0.933333,1,0.941176,1,0.888889,0.625,0.866667,0.789474,0.565217,0.411765,0.789474,0.941176,0.733333,0.882353,1,0.842105,0.9375,1,0.923077,0.882353,0.722222,0.928571,0.823529,0.866667,1,0.75,0.769231,0.538462,1,0.9375,0.611111,0.722222,0.888889,0.9375,0.705882,0.842105,0.882353,0.785714,0.357143,1,0.857143,0.733333,0.928571,1,0.666667,1,0.8125,0.9375,0.9375,0.588235,1,0.941176,1,0.866667,1,0.666667,0.875,0.933333,1,0.777778,0.933333,1,0.857143,0.882353,0.9375,0.882353,1,0.875,0.823529,0.681818,0.666667,0.933333]
predictive_accuracy
0.8375
prior_entropy
6.6438561897747395
recall
0.8375 [0.8125,0.9375,1,1,0.875,0.75,1,1,0.875,0.9375,0.75,1,1,0.625,1,0.8125,1,0.5,0.4375,0.125,0.8125,0.9375,0.9375,1,0.9375,0.4375,0.5,0.9375,0.875,1,1,1,1,0.625,0.8125,0.9375,0.8125,0.4375,0.9375,1,0.6875,0.9375,1,1,0.9375,0.9375,0.75,0.9375,0.8125,0.8125,0.875,0.8125,0.8125,0.75,0.625,0.4375,1,0.9375,0.6875,0.8125,1,0.9375,0.75,1,0.9375,0.6875,0.3125,1,0.75,0.6875,0.8125,0.75,0.875,0.9375,0.8125,0.9375,0.9375,0.625,0.9375,1,0.875,0.8125,0.8125,0.75,0.875,0.875,0.9375,0.875,0.875,1,0.75,0.9375,0.9375,0.9375,0.9375,0.875,0.875,0.9375,0.375,0.875]
relative_absolute_error
0.16414141414141312
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.05700877125495677
root_relative_squared_error
0.5729597091269387
total_cost
0
area_under_roc_curve
0.899072526072765 [0.993711,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,0.75,1,0.5,1,0.75,0.746835,0.493711,0.5,1,0.993711,1,1,0.746835,0.746835,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,0.996855,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,1,1,1,1,0.996855,0.496855,1,1,0.5,1,1,1,0.75,1,1,1,0.496835,1,1,0.5,0.75,1,1,1,0.996855,0.996835,0.75,0.5,1,1,1,0.996835,1,0.996855,1,0.75,0.75,1,0.75,1,0.996835,1,1,0.996835,1,0.746835,0.496855,0.993671,0.496835,1]
area_under_roc_curve
0.9337035267892683 [1,1,1,1,1,1,1,1,1,1,0.75,1,1,0.75,1,1,1,0.746835,0.75,0.496855,1,1,0.996855,1,1,0.746835,0.493671,1,1,1,1,1,1,1,0.75,0.996855,0.996855,0.496855,1,1,1,1,1,1,1,1,1,1,0.746835,1,0.993671,1,1,0.75,1,0.496855,1,1,0.996855,0.75,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.75,0.75,1,1,0.746835,1,1,0.996835,0.75,1,0.75,1,1,1,1,1,1,0.996835,0.75,0.496855]
area_under_roc_curve
0.930538969827243 [1,1,1,1,1,1,1,1,1,1,0.75,1,1,0.75,1,1,1,0.743671,1,0.490506,1,1,1,1,1,0.743671,1,1,1,1,1,1,1,0.496835,0.996855,0.996855,0.993711,0.5,1,1,0.746835,0.5,1,0.996855,0.996835,1,0.75,1,0.75,1,1,1,1,1,1,0.75,1,1,1,0.746835,1,1,1,0.996855,1,1,0.5,1,0.5,0.75,1,1,0.996855,1,1,1,1,0.746835,0.75,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.996855,1,1,1,1,1,0.996835,0.5,1]
area_under_roc_curve
0.9116710452989409 [0.75,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,1,0.75,1,1,1,0.740506,0.493711,0.496835,1,1,0.996835,1,1,0.75,1,1,1,1,1,1,1,0.75,1,0.996855,1,0.493711,0.996855,1,1,0.996855,1,1,1,1,0.75,1,0.996835,1,1,1,1,0.746835,1,0.75,1,1,0.996855,1,1,1,0.996835,1,1,0.746835,0.493671,1,1,0.746835,0.5,0.75,0.990566,1,1,1,1,0.75,1,1,1,1,0.75,0.75,1,1,1,1,1,1,0.75,1,0.996855,1,1,0.996855,0.75,0.996835,0.996855,0.75]
area_under_roc_curve
0.9115715309290661 [0.996835,1,1,1,1,0.75,1,1,1,1,1,1,1,0.496855,1,1,1,0.496835,0.996855,0.743671,1,1,1,1,1,0.996835,0.5,1,0.996855,1,1,1,1,0.746835,1,1,0.996835,0.743671,1,1,0.746835,1,1,1,1,1,1,1,1,1,1,0.496855,0.75,1,0.496835,0.5,1,0.996835,0.746835,1,1,1,0.5,1,0.75,1,0.496855,1,0.75,0.746835,1,1,0.996855,1,0.75,1,0.996835,0.746835,1,1,1,1,1,0.743671,0.996855,1,1,1,1,1,1,0.75,1,1,0.75,1,0.996835,0.996855,0.496855,1]
area_under_roc_curve
0.9210652018151422 [0.75,0.996855,1,1,1,0.75,1,1,1,0.75,0.75,0.996835,1,1,1,1,1,0.996835,1,0.75,0.75,0.75,0.993671,1,0.996835,0.743671,0.746835,0.5,1,1,1,1,1,0.996835,1,0.75,0.75,0.743671,1,1,1,1,1,0.996855,1,1,1,1,0.75,0.75,1,1,1,0.996855,0.996835,0.996835,1,1,0.75,0.996855,1,1,0.993711,1,1,0.5,0.996855,1,0.746835,1,1,0.75,1,1,0.75,1,1,1,1,1,1,0.75,0.75,0.996835,1,1,1,0.996855,0.996835,1,0.5,1,1,1,1,1,1,0.996855,0.5,1]
area_under_roc_curve
0.9241899530292172 [0.996835,1,1,1,1,0.75,1,1,0.996855,1,1,1,1,1,1,0.75,1,0.996855,0.493671,0.5,0.996835,1,1,1,0.996835,0.5,0.493711,1,1,1,0.996835,1,1,1,1,1,0.743671,0.75,0.990506,1,1,1,1,1,0.5,0.75,0.5,0.996835,0.996855,1,1,1,1,1,0.75,0.75,1,1,0.5,1,0.993671,1,0.996855,0.996835,1,0.5,0.996855,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.5,1,1,1,1,0.75,1,1,0.75,1,1,0.5,1]
area_under_roc_curve
0.9149152137568664 [0.75,1,1,1,1,1,1,1,1,1,1,1,1,0.996855,1,0.75,1,0.993711,0.75,0.5,0.75,1,1,1,0.75,0.5,0.996855,1,0.5,1,1,1,0.996855,1,1,1,0.993671,0.996835,1,0.996855,0.5,1,1,1,1,1,1,0.75,0.996855,0.5,0.496855,0.75,0.75,0.996855,1,0.743671,1,1,1,0.990566,1,0.996835,1,1,1,0.496855,0.496855,1,1,0.746835,1,1,0.996835,1,0.996835,1,1,0.493711,1,1,0.75,1,0.75,1,0.75,0.75,1,0.996855,1,1,0.5,1,1,0.996855,1,1,1,1,0.75,1]
area_under_roc_curve
0.930598678449168 [0.996855,1,1,1,1,0.996855,1,1,0.75,1,1,1,1,1,1,1,1,0.496855,0.75,0.496855,1,1,0.5,1,1,0.493711,0.996855,0.996835,1,1,1,1,1,0.996855,0.996835,1,1,0.496835,0.75,1,0.5,1,1,0.996835,1,1,0.996855,1,1,1,0.75,0.996835,0.75,0.75,0.5,0.5,1,0.5,0.993671,1,1,1,1,0.996835,1,1,0.743671,1,0.996835,0.996855,1,0.5,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1]
area_under_roc_curve
0.9022967916567153 [0.996855,1,1,1,1,1,1,1,1,1,0.996855,1,1,0.75,1,1,1,0.490566,0.5,0.493711,1,1,1,1,1,0.493711,0.5,0.996835,0.75,1,1,1,1,0.996855,0.75,1,0.746835,0.75,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,1,0.75,1,0.746835,0.75,0.996855,1,1,0.993671,0.75,1,0.75,0.496835,1,0.993711,0.996855,0.5,1,0.75,1,1,0.5,0.746835,0.75,0.75,0.75,1,1,1,1,1,0.496855,1,0.496855,1,0.996835,1,0.746835,1,1,0.996835,0.996835,1,0.75,1,1,0.996855,0.75,1,1]
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
f_measure
0.8674999999999997 [1,1,1,1,1,1,1,1,1,1,0.666667,1,1,0.666667,1,1,1,0.5,0.666667,0,1,1,0.666667,1,1,0.5,0,1,1,1,1,1,1,1,0.666667,0.666667,0.666667,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.666667,1,1,0.666667,1,0,1,1,0.666667,0.666667,1,1,1,1,1,1,1,1,1,1,0.8,1,1,1,1,1,1,0.666667,1,1,0.666667,0.666667,1,1,0.5,1,1,0.8,0.666667,1,0.666667,1,1,1,1,1,1,0.8,0.666667,0]
kappa
0.7978681405448086
kappa
0.8673195387774444
kappa
0.8610014215763703
kappa
0.8231485867677246
kappa
0.8230927183699257
kappa
0.8420595435520809
kappa
0.8483532106468683
kappa
0.8294512435846823
kappa
0.8609904430929627
kappa
0.8042079501046067
kb_relative_information_score
127.93016311356081
kb_relative_information_score
138.95416954327428
kb_relative_information_score
137.951987140573
kb_relative_information_score
131.9388927243657
kb_relative_information_score
131.9388927243657
kb_relative_information_score
134.94543993246936
kb_relative_information_score
135.94762233517056
kb_relative_information_score
132.94107512706694
kb_relative_information_score
137.951987140573
kb_relative_information_score
128.93234551626205
mean_absolute_error
0.004000000000000002
mean_absolute_error
0.0026250000000000006
mean_absolute_error
0.0027500000000000007
mean_absolute_error
0.003500000000000001
mean_absolute_error
0.003500000000000001
mean_absolute_error
0.0031250000000000006
mean_absolute_error
0.003000000000000001
mean_absolute_error
0.003375000000000001
mean_absolute_error
0.0027500000000000007
mean_absolute_error
0.0038750000000000013
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]
precision
0.9 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.666667,1,1,1,1,1,1,1,1,1,0.666667,1,0]
predictive_accuracy
0.8
predictive_accuracy
0.86875
predictive_accuracy
0.8625
predictive_accuracy
0.825
predictive_accuracy
0.825
predictive_accuracy
0.84375
predictive_accuracy
0.85
predictive_accuracy
0.83125
predictive_accuracy
0.8625
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
0.8 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,0,1,0.5,0.5,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1]
recall
0.86875 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,0]
recall
0.8625 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,0,1,0,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1]
recall
0.825 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0.5,0,0.5,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5]
recall
0.825 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,0,1,1,0.5,1,1,1,0,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1]
recall
0.84375 [0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,0.5,1,1,0.5,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1]
recall
0.85 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1]
recall
0.83125 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1]
recall
0.8625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,0,0,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1]
recall
0.80625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,1,0.5,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1]
relative_absolute_error
0.2020202020202018
relative_absolute_error
0.13257575757575737
relative_absolute_error
0.1388888888888887
relative_absolute_error
0.17676767676767655
relative_absolute_error
0.17676767676767655
relative_absolute_error
0.1578282828282826
relative_absolute_error
0.1515151515151513
relative_absolute_error
0.17045454545454522
relative_absolute_error
0.1388888888888887
relative_absolute_error
0.19570707070707044
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
0.0632455532033676
root_mean_squared_error
0.051234753829797995
root_mean_squared_error
0.052440442408507586
root_mean_squared_error
0.05916079783099617
root_mean_squared_error
0.05916079783099617
root_mean_squared_error
0.05590169943749475
root_mean_squared_error
0.05477225575051662
root_mean_squared_error
0.058094750193111264
root_mean_squared_error
0.052440442408507586
root_mean_squared_error
0.06224949798994367
root_relative_squared_error
0.6356417261637279
root_relative_squared_error
0.5149286505444369
root_relative_squared_error
0.5270462766947296
root_relative_squared_error
0.5945883900105629
root_relative_squared_error
0.5945883900105629
root_relative_squared_error
0.5618332187193681
root_relative_squared_error
0.5504818825631801
root_relative_squared_error
0.5838742081211419
root_relative_squared_error
0.5270462766947296
root_relative_squared_error
0.6256309946079566
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
439.5810559999518
usercpu_time_millis
444.83544399986386
usercpu_time_millis
437.91201099998034
usercpu_time_millis
454.3736909997733
usercpu_time_millis
453.28515599999264
usercpu_time_millis
457.8738609998254
usercpu_time_millis
437.19166400001086
usercpu_time_millis
445.8092659999693
usercpu_time_millis
430.33586299998206
usercpu_time_millis
442.94973299997764
usercpu_time_millis_testing
57.319350999932794
usercpu_time_millis_testing
56.463076
usercpu_time_millis_testing
53.69332999998733
usercpu_time_millis_testing
55.27115499990032
usercpu_time_millis_testing
55.343799999945986
usercpu_time_millis_testing
53.96093599983942
usercpu_time_millis_testing
59.31044399994789
usercpu_time_millis_testing
53.64065499998105
usercpu_time_millis_testing
55.75676300009036
usercpu_time_millis_testing
53.60855399999309
usercpu_time_millis_training
382.261705000019
usercpu_time_millis_training
388.37236799986385
usercpu_time_millis_training
384.218680999993
usercpu_time_millis_training
399.102535999873
usercpu_time_millis_training
397.94135600004665
usercpu_time_millis_training
403.912924999986
usercpu_time_millis_training
377.88122000006297
usercpu_time_millis_training
392.16861099998823
usercpu_time_millis_training
374.5790999998917
usercpu_time_millis_training
389.34117899998455