8958186
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)
6893794
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
169.3839088640749
7650
cache_size
200
7650
class_weight
null
7650
coef0
0.620207650889114
7650
decision_function_shape
"ovr"
7650
degree
3
7650
gamma
0.0700115965229378
7650
kernel
"rbf"
7650
max_iter
-1
7650
probability
false
7650
random_state
1
7650
shrinking
true
7650
tol
0.003025733011574462
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
18831640
description
https://api.openml.org/data/download/18831640/description.xml
-1
18831641
predictions
https://api.openml.org/data/download/18831641/predictions.arff
area_under_roc_curve
0.9160353535353535 [0.874369,0.96875,0.9375,0.964015,0.9375,0.841856,1,0.96875,0.874684,0.9375,0.968434,0.968434,0.96875,0.90625,0.933396,0.968434,1,0.747159,0.746528,0.651515,0.905619,0.9375,0.967172,0.96875,0.937184,0.747475,0.71654,0.967172,0.936869,1,0.968434,0.96875,0.999369,0.809975,0.936869,0.936553,0.778725,0.71654,0.968119,0.968434,0.904987,0.968119,1,0.967803,0.9375,0.96875,0.936869,0.999369,0.936237,0.905934,0.874053,0.905303,0.874684,0.843119,0.936237,0.748106,0.96875,0.968434,0.81029,0.873422,0.999369,0.968434,0.842803,0.999369,0.936869,0.842487,0.717172,1,0.968119,0.936553,0.874053,0.936869,0.93529,0.9375,0.905619,0.96875,0.968434,0.810922,0.96875,0.968434,0.937184,0.905619,0.875,0.810922,0.905619,0.968434,1,0.936869,0.937184,0.96875,0.905619,0.968434,0.9375,0.937184,0.96875,0.937184,0.873737,0.967803,0.810922,0.937184]
average_cost
0
f_measure
0.8361291147481726 [0.8,0.967742,0.933333,0.652174,0.933333,0.666667,1,0.967742,0.827586,0.933333,0.9375,0.9375,0.967742,0.896552,0.651163,0.9375,1,0.484848,0.457143,0.277778,0.83871,0.933333,0.833333,0.967742,0.903226,0.5,0.466667,0.833333,0.875,1,0.9375,0.967742,0.941176,0.588235,0.875,0.848485,0.545455,0.466667,0.909091,0.9375,0.787879,0.909091,1,0.882353,0.933333,0.967742,0.875,0.941176,0.823529,0.866667,0.774194,0.8125,0.827586,0.758621,0.823529,0.533333,0.967742,0.9375,0.606061,0.727273,0.941176,0.9375,0.733333,0.941176,0.875,0.709677,0.5,1,0.909091,0.848485,0.774194,0.875,0.756757,0.933333,0.83871,0.967742,0.9375,0.645161,0.967742,0.9375,0.903226,0.83871,0.857143,0.645161,0.83871,0.9375,1,0.875,0.903226,0.967742,0.83871,0.9375,0.933333,0.903226,0.967742,0.903226,0.75,0.882353,0.645161,0.903226]
kappa
0.8320707070707071
kb_relative_information_score
1333.419480881466
mean_absolute_error
0.0033249999999999842
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
0.8449973598526229 [0.857143,1,1,0.5,1,0.647059,1,1,0.923077,1,0.9375,0.9375,1,1,0.518519,0.9375,1,0.470588,0.421053,0.25,0.866667,1,0.75,1,0.933333,0.5,0.5,0.75,0.875,1,0.9375,1,0.888889,0.555556,0.875,0.823529,0.529412,0.5,0.882353,0.9375,0.764706,0.882353,1,0.833333,1,1,0.875,0.888889,0.777778,0.928571,0.8,0.8125,0.923077,0.846154,0.777778,0.571429,1,0.9375,0.588235,0.705882,0.888889,0.9375,0.785714,0.888889,0.875,0.733333,0.583333,1,0.882353,0.823529,0.8,0.875,0.666667,1,0.866667,1,0.9375,0.666667,1,0.9375,0.933333,0.866667,1,0.666667,0.866667,0.9375,1,0.875,0.933333,1,0.866667,0.9375,1,0.933333,1,0.933333,0.75,0.833333,0.666667,0.933333]
predictive_accuracy
0.83375
prior_entropy
6.6438561897747395
recall
0.83375 [0.75,0.9375,0.875,0.9375,0.875,0.6875,1,0.9375,0.75,0.875,0.9375,0.9375,0.9375,0.8125,0.875,0.9375,1,0.5,0.5,0.3125,0.8125,0.875,0.9375,0.9375,0.875,0.5,0.4375,0.9375,0.875,1,0.9375,0.9375,1,0.625,0.875,0.875,0.5625,0.4375,0.9375,0.9375,0.8125,0.9375,1,0.9375,0.875,0.9375,0.875,1,0.875,0.8125,0.75,0.8125,0.75,0.6875,0.875,0.5,0.9375,0.9375,0.625,0.75,1,0.9375,0.6875,1,0.875,0.6875,0.4375,1,0.9375,0.875,0.75,0.875,0.875,0.875,0.8125,0.9375,0.9375,0.625,0.9375,0.9375,0.875,0.8125,0.75,0.625,0.8125,0.9375,1,0.875,0.875,0.9375,0.8125,0.9375,0.875,0.875,0.9375,0.875,0.75,0.9375,0.625,0.875]
relative_absolute_error
0.16792929292929182
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.05766281297335384
root_relative_squared_error
0.5795330757244004
total_cost
0
area_under_roc_curve
0.9022370830347904 [0.996855,0.75,1,0.996855,0.75,0.996855,1,1,0.75,1,0.75,1,1,1,1,0.5,1,0.75,0.496835,0.490566,0.5,1,0.996855,1,1,0.746835,0.743671,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,0.996855,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,0.996835,1,1,1,0.996855,0.496855,1,1,0.496855,1,1,1,0.5,1,1,1,0.5,1,1,0.5,0.75,0.996855,1,1,1,1,0.75,0.5,1,1,1,0.996835,1,1,1,1,1,1,0.75,1,0.996835,1,1,0.996835,1,0.75,0.5,0.993671,0.746835,1]
area_under_roc_curve
0.9211647161850168 [1,1,1,0.987421,1,1,1,1,1,1,1,1,1,0.75,0.75,0.996855,1,0.75,0.746835,0.490566,1,1,0.996855,1,1,0.746835,0.493671,1,0.996835,1,1,1,1,0.996835,1,0.496855,0.496855,0.496855,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.75,1,0.496855,1,1,1,0.75,1,1,1,1,1,0.996835,0.75,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,0.75,0.75,0.75,1,1,0.75,1,1,0.996835,0.75,1,0.75,1,0.75,1,1,1,0.5,1,0.75,0.496855]
area_under_roc_curve
0.930618581323143 [1,1,0.75,0.984277,1,0.746835,1,1,1,1,1,0.75,1,1,1,1,1,0.743671,1,0.743671,1,0.5,0.75,1,1,0.746835,0.75,0.996855,1,1,1,1,1,0.75,0.993711,1,0.996855,0.5,1,1,0.75,1,1,1,1,1,0.75,1,0.75,1,1,1,1,1,1,1,1,1,0.996855,0.496835,1,1,0.75,1,1,1,1,1,1,0.75,1,0.996835,0.996855,1,0.996855,1,1,0.746835,0.75,0.996835,1,1,1,0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1,1]
area_under_roc_curve
0.9116909481729161 [0.75,1,0.75,0.996855,0.75,0.746835,1,1,0.746835,1,1,1,1,1,1,1,1,0.743671,0.493711,0.746835,1,1,0.993671,1,0.5,1,1,1,1,1,1,1,1,0.75,1,1,1,0.496855,1,1,1,0.996855,1,0.996855,1,1,1,1,1,1,0.996835,1,1,0.75,0.996855,0.743671,1,1,0.996855,1,0.996855,1,1,1,0.75,0.5,0.496835,1,1,0.996835,0.5,1,0.990566,1,1,1,1,0.75,1,1,1,1,0.75,0.75,0.5,1,1,1,1,1,0.75,1,1,1,1,0.996855,0.5,0.996835,0.993711,0.75]
area_under_roc_curve
0.911631239550991 [0.75,1,1,1,1,0.75,1,0.75,1,1,0.996835,1,1,0.5,0.984277,1,1,0.746835,0.993711,0.746835,1,1,1,1,1,0.993671,0.5,1,0.996855,1,1,0.5,1,0.746835,1,1,0.746835,0.75,1,1,0.996835,1,1,1,0.5,1,1,1,1,1,0.5,0.496855,0.75,0.996855,0.746835,0.5,1,0.996835,0.746835,1,1,1,1,1,0.75,1,1,1,1,1,0.996835,1,1,0.5,0.75,1,0.996835,1,1,1,1,1,0.75,0.746835,0.996855,1,1,1,1,1,1,1,1,0.5,0.75,1,0.993671,1,0.496855,1]
area_under_roc_curve
0.9115715309290662 [0.75,1,1,0.75,1,0.75,1,1,1,0.75,1,0.996835,0.5,1,0.990566,1,1,0.996835,0.996855,0.75,0.75,0.75,0.996835,1,0.996835,0.746835,0.746835,0.5,1,1,1,1,1,0.746835,1,0.746835,0.5,0.743671,1,1,0.996835,1,1,0.996855,1,1,0.996835,1,0.75,0.75,1,1,0.75,1,0.996835,0.996835,1,1,0.75,0.996855,1,0.75,0.996855,1,1,0.75,1,1,0.996835,1,0.75,1,1,1,0.75,1,1,0.746835,1,1,0.996855,0.75,0.75,0.996835,1,1,1,0.996855,0.996835,0.75,1,1,1,1,1,1,0.996835,1,1,1]
area_under_roc_curve
0.921065201815142 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.496835,0.5,0.993671,1,1,1,1,0.5,0.496855,1,1,1,0.746835,1,1,0.996855,1,1,0.493671,0.746835,0.993671,0.5,1,0.75,1,1,0.5,0.75,0.75,0.996835,1,1,1,0.996835,0.996835,0.5,1,0.75,1,1,0.5,1,0.996835,1,0.996855,0.996835,1,0.496855,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.5,1,1,0.996855,1,0.75,1,1,0.75,1,1,0.5,1]
area_under_roc_curve
0.9116909481729161 [0.75,1,1,1,1,0.75,1,1,1,1,1,1,1,1,0.990566,1,1,0.496855,0.990506,0.496835,0.75,1,1,1,0.75,0.5,1,1,0.5,1,1,1,0.996855,1,1,1,0.746835,0.746835,1,0.996855,0.75,1,1,1,1,1,1,1,0.993711,0.5,0.496855,0.75,0.75,0.996855,1,0.746835,0.75,1,0.996835,0.990566,1,0.996835,1,1,1,0.496855,0.5,1,1,0.996835,1,1,0.996835,1,0.996835,1,1,0.496855,1,1,0.75,1,0.75,0.75,0.75,0.75,1,0.5,1,1,1,0.75,1,1,1,1,1,1,0.996835,1]
area_under_roc_curve
0.9243093702730674 [0.996855,1,1,0.993671,1,0.993711,1,1,0.75,1,1,1,1,1,0.75,1,1,0.493711,1,0.496855,1,1,1,0.75,1,0.496855,0.996855,0.993671,1,1,1,1,1,0.996855,1,1,1,0.5,0.75,1,0.5,1,1,0.746835,1,1,0.996855,1,1,1,0.75,0.996835,0.75,0.75,0.75,0.5,1,0.5,0.746835,1,1,1,1,0.996835,1,1,0.743671,1,0.996835,1,0.996855,0.5,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.496855,1,1,1,1,1,1,0.75,1,1,1,1,1,1,1,0.75,1]
area_under_roc_curve
0.9148953108828914 [1,1,1,0.993671,1,0.996855,1,1,0.75,0.5,1,1,1,0.75,1,1,1,0.5,0.5,0.990566,1,1,1,1,1,0.496855,0.5,0.996835,0.75,1,1,1,1,0.993711,0.75,1,0.996835,1,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.75,1,1,1,1,0.996835,0.75,1,1,0.496835,1,0.993711,0.996855,0.5,1,0.75,0.996855,1,0.5,0.746835,0.75,0.75,0.75,1,1,1,1,1,0.496855,1,0.496855,0.996835,0.996835,1,1,1,1,1,0.996835,1,0.75,1,1,0.996855,1,1,1]
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
0.8041847611527833
kappa
0.8420657796027955
kappa
0.8610343466245559
kappa
0.8231416051478425
kappa
0.8230927183699257
kappa
0.8231136731551308
kappa
0.8420345944238212
kappa
0.8231066887783305
kappa
0.8483711747285291
kappa
0.8294512435846823
kb_relative_information_score
128.93234551626202
kb_relative_information_score
134.9454399324694
kb_relative_information_score
137.951987140573
kb_relative_information_score
131.9388927243657
kb_relative_information_score
131.9388927243657
kb_relative_information_score
131.93889272436567
kb_relative_information_score
134.94543993246936
kb_relative_information_score
131.93889272436567
kb_relative_information_score
135.9476223351706
kb_relative_information_score
132.94107512706694
mean_absolute_error
0.0038750000000000013
mean_absolute_error
0.0031250000000000006
mean_absolute_error
0.0027500000000000007
mean_absolute_error
0.003500000000000001
mean_absolute_error
0.003500000000000001
mean_absolute_error
0.003500000000000001
mean_absolute_error
0.0031250000000000006
mean_absolute_error
0.003500000000000001
mean_absolute_error
0.003000000000000001
mean_absolute_error
0.003375000000000001
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
0.84375
predictive_accuracy
0.8625
predictive_accuracy
0.825
predictive_accuracy
0.825
predictive_accuracy
0.825
predictive_accuracy
0.84375
predictive_accuracy
0.825
predictive_accuracy
0.85
predictive_accuracy
0.83125
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 [1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,0,1,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.84375 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,0,1,0.5,0]
recall
0.8625 [1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,0,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1]
recall
0.825 [0.5,1,0.5,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5]
recall
0.825 [0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0,0,0.5,1,0.5,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0,1]
recall
0.825 [0.5,1,1,0.5,1,0.5,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1]
recall
0.84375 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0,0.5,1,0,1,0.5,1,1,0,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1]
recall
0.825 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0,0,0.5,0.5,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1]
recall
0.85 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1]
recall
0.83125 [1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1]
relative_absolute_error
0.19570707070707044
relative_absolute_error
0.1578282828282826
relative_absolute_error
0.1388888888888887
relative_absolute_error
0.17676767676767655
relative_absolute_error
0.17676767676767655
relative_absolute_error
0.17676767676767655
relative_absolute_error
0.1578282828282826
relative_absolute_error
0.17676767676767655
relative_absolute_error
0.1515151515151513
relative_absolute_error
0.17045454545454522
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
0.06224949798994367
root_mean_squared_error
0.05590169943749475
root_mean_squared_error
0.052440442408507586
root_mean_squared_error
0.05916079783099617
root_mean_squared_error
0.05916079783099617
root_mean_squared_error
0.05916079783099617
root_mean_squared_error
0.05590169943749475
root_mean_squared_error
0.05916079783099617
root_mean_squared_error
0.05477225575051662
root_mean_squared_error
0.058094750193111264
root_relative_squared_error
0.6256309946079566
root_relative_squared_error
0.5618332187193681
root_relative_squared_error
0.5270462766947296
root_relative_squared_error
0.5945883900105629
root_relative_squared_error
0.5945883900105629
root_relative_squared_error
0.5945883900105629
root_relative_squared_error
0.5618332187193681
root_relative_squared_error
0.5945883900105629
root_relative_squared_error
0.5504818825631801
root_relative_squared_error
0.5838742081211419
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
710.2111199998262
usercpu_time_millis
710.4735310000478
usercpu_time_millis
713.2256250000637
usercpu_time_millis
729.7495349999963
usercpu_time_millis
704.6213339999667
usercpu_time_millis
734.6384840000155
usercpu_time_millis
744.7043349999376
usercpu_time_millis
740.4356010000583
usercpu_time_millis
730.7175299999926
usercpu_time_millis
728.085845999999
usercpu_time_millis_testing
53.08420299991212
usercpu_time_millis_testing
55.02969600001961
usercpu_time_millis_testing
52.82073600005788
usercpu_time_millis_testing
57.010015999935604
usercpu_time_millis_testing
53.7075280000181
usercpu_time_millis_testing
56.07348199998796
usercpu_time_millis_testing
56.07502899999872
usercpu_time_millis_testing
55.87577699998292
usercpu_time_millis_testing
54.29441399996904
usercpu_time_millis_testing
54.59722199998396
usercpu_time_millis_training
657.1269169999141
usercpu_time_millis_training
655.4438350000282
usercpu_time_millis_training
660.4048890000058
usercpu_time_millis_training
672.7395190000607
usercpu_time_millis_training
650.9138059999486
usercpu_time_millis_training
678.5650020000276
usercpu_time_millis_training
688.6293059999389
usercpu_time_millis_training
684.5598240000754
usercpu_time_millis_training
676.4231160000236
usercpu_time_millis_training
673.4886240000151