8958879
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)
6894483
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
55.17565768775013
7650
cache_size
200
7650
class_weight
null
7650
coef0
0.5392980008130204
7650
decision_function_shape
"ovr"
7650
degree
3
7650
gamma
0.025512781655979835
7650
kernel
"rbf"
7650
max_iter
-1
7650
probability
false
7650
random_state
1
7650
shrinking
false
7650
tol
0.0014361242090324337
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
18833016
description
https://api.openml.org/data/download/18833016/description.xml
-1
18833017
predictions
https://api.openml.org/data/download/18833017/predictions.arff
area_under_roc_curve
0.9207702020202018 [0.842803,0.96875,1,0.9375,0.937184,0.905303,0.999684,1,0.905934,0.9375,0.967803,0.968434,1,0.90625,0.998422,0.96875,1,0.81029,0.714646,0.620581,0.874053,0.96875,0.967172,1,0.937184,0.746843,0.748422,0.936553,0.937184,1,0.999684,0.96875,0.999369,0.778093,0.905934,0.937184,0.74779,0.716856,0.968434,1,0.935606,0.936869,1,0.936553,0.968434,0.96875,0.968434,0.968119,0.873422,0.905303,0.904672,0.905619,0.937184,0.843119,0.874053,0.809975,1,0.968434,0.903725,0.873422,0.936869,0.968434,0.842803,0.968119,0.968119,0.810606,0.716856,1,0.968119,0.873422,0.874053,0.968434,0.93529,0.96875,0.937184,0.96875,0.968434,0.841856,1,1,0.905934,0.936553,0.905934,0.748737,0.905303,1,1,0.935922,0.937184,1,0.905619,0.999684,0.968434,0.937184,0.96875,0.936869,0.936553,0.905303,0.779987,0.937184]
average_cost
0
f_measure
0.8440403116762387 [0.733333,0.967742,1,0.933333,0.903226,0.8125,0.969697,1,0.866667,0.933333,0.882353,0.9375,1,0.896552,0.864865,0.967742,1,0.606061,0.388889,0.235294,0.774194,0.967742,0.833333,1,0.903226,0.470588,0.551724,0.848485,0.903226,1,0.969697,0.967742,0.941176,0.514286,0.866667,0.903226,0.516129,0.482759,0.9375,1,0.777778,0.875,1,0.848485,0.9375,0.967742,0.9375,0.909091,0.727273,0.8125,0.764706,0.83871,0.903226,0.758621,0.774194,0.588235,1,0.9375,0.702703,0.727273,0.875,0.9375,0.733333,0.909091,0.909091,0.625,0.482759,1,0.909091,0.727273,0.774194,0.9375,0.756757,0.967742,0.903226,0.967742,0.9375,0.666667,1,1,0.866667,0.848485,0.866667,0.571429,0.8125,1,1,0.8,0.903226,1,0.83871,0.969697,0.9375,0.903226,0.967742,0.875,0.848485,0.8125,0.62069,0.903226]
kappa
0.8415404040404041
kb_relative_information_score
1348.452216921983
mean_absolute_error
0.003137499999999988
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
0.8491212887989206 [0.785714,1,1,1,0.933333,0.8125,0.941176,1,0.928571,1,0.833333,0.9375,1,1,0.761905,1,1,0.588235,0.35,0.222222,0.8,1,0.75,1,0.933333,0.444444,0.615385,0.823529,0.933333,1,0.941176,1,0.888889,0.473684,0.928571,0.933333,0.533333,0.538462,0.9375,1,0.7,0.875,1,0.823529,0.9375,1,0.9375,0.882353,0.705882,0.8125,0.722222,0.866667,0.933333,0.846154,0.8,0.555556,1,0.9375,0.619048,0.705882,0.875,0.9375,0.785714,0.882353,0.882353,0.625,0.538462,1,0.882353,0.705882,0.8,0.9375,0.666667,1,0.933333,1,0.9375,0.647059,1,1,0.928571,0.823529,0.928571,0.666667,0.8125,1,1,0.736842,0.933333,1,0.866667,0.941176,0.9375,0.933333,1,0.875,0.823529,0.8125,0.692308,0.933333]
predictive_accuracy
0.843125
prior_entropy
6.6438561897747395
recall
0.843125 [0.6875,0.9375,1,0.875,0.875,0.8125,1,1,0.8125,0.875,0.9375,0.9375,1,0.8125,1,0.9375,1,0.625,0.4375,0.25,0.75,0.9375,0.9375,1,0.875,0.5,0.5,0.875,0.875,1,1,0.9375,1,0.5625,0.8125,0.875,0.5,0.4375,0.9375,1,0.875,0.875,1,0.875,0.9375,0.9375,0.9375,0.9375,0.75,0.8125,0.8125,0.8125,0.875,0.6875,0.75,0.625,1,0.9375,0.8125,0.75,0.875,0.9375,0.6875,0.9375,0.9375,0.625,0.4375,1,0.9375,0.75,0.75,0.9375,0.875,0.9375,0.875,0.9375,0.9375,0.6875,1,1,0.8125,0.875,0.8125,0.5,0.8125,1,1,0.875,0.875,1,0.8125,1,0.9375,0.875,0.9375,0.875,0.875,0.8125,0.5625,0.875]
relative_absolute_error
0.15845959595959505
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.05601339125602009
root_relative_squared_error
0.5629557637320983
total_cost
0
area_under_roc_curve
0.9148555051349417 [0.996855,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,1,1,0.5,1,0.75,0.496835,0.987421,0.5,1,0.996855,1,1,0.743671,0.75,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,1,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,1,1,1,1,0.996855,0.496855,1,1,1,1,1,1,0.75,1,1,1,0.5,1,1,0.5,0.75,1,1,1,1,1,0.75,0.5,1,1,1,1,1,0.996855,1,1,1,0.996835,0.75,1,0.996835,1,1,0.996835,1,0.75,0.496855,0.993671,0.746835,1]
area_under_roc_curve
0.9147360878910913 [1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.75,1,1,1,0.75,0.75,0.487421,1,1,0.996855,1,1,0.746835,0.493671,1,0.996835,1,1,1,1,0.990506,0.75,0.5,0.496855,0.5,1,1,1,1,1,0.75,1,1,1,1,0.746835,0.996835,0.993671,1,1,0.75,1,0.496855,1,1,1,0.5,1,1,0.75,1,1,0.75,0.75,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.75,0.996835,0.996855,1,0.746835,1,1,0.996835,0.75,1,0.75,1,1,0.75,1,1,1,1,0.75,0.496855]
area_under_roc_curve
0.9463617546373694 [1,1,1,1,1,0.746835,1,1,1,1,1,0.75,1,1,0.996835,1,1,0.996835,1,0.5,1,1,1,1,1,0.746835,1,0.996855,1,1,1,1,1,0.75,0.996855,1,0.993711,1,1,1,0.746835,0.5,1,1,0.996835,1,0.75,1,0.75,1,1,1,1,1,1,1,1,1,0.993711,0.746835,1,1,0.75,0.5,1,1,1,1,1,0.75,1,0.996835,1,1,0.996855,1,1,0.996835,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,0.996855,1]
area_under_roc_curve
0.9147957965130162 [0.5,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,1,1,1,1,1,0.743671,0.493711,0.75,1,1,0.993671,1,0.5,1,1,1,1,1,1,1,1,0.75,1,1,1,0.496855,1,1,0.996835,0.996855,1,0.996855,1,1,1,1,0.996835,1,0.996835,1,1,0.75,1,0.993671,1,1,0.996855,1,0.996855,1,1,1,0.75,0.5,0.493671,1,1,0.743671,0.5,1,0.990566,1,1,1,1,0.75,1,1,1,1,0.75,0.75,0.5,1,1,1,1,1,0.75,1,1,1,1,0.996855,0.75,0.996835,0.996855,0.75]
area_under_roc_curve
0.9146962821431416 [0.75,1,1,1,1,0.75,1,1,1,1,0.996835,1,1,0.5,0.996855,1,1,0.746835,0.990566,0.740506,0.75,1,1,1,1,0.990506,0.5,1,1,1,1,0.5,1,0.496835,1,1,0.746835,0.5,1,1,0.746835,1,1,0.5,1,1,1,1,1,1,0.996855,0.496855,0.75,1,0.496835,0.5,1,0.996835,0.993671,1,1,1,0.5,1,1,1,1,1,1,0.996835,0.996835,1,1,1,0.75,1,0.996835,0.996835,1,1,1,1,1,0.75,0.996855,1,1,1,1,1,1,1,1,1,0.75,1,0.996835,1,0.5,1]
area_under_roc_curve
0.9147161850171164 [0.75,1,1,0.75,1,1,1,1,1,0.75,1,0.996835,1,1,0.996855,1,1,1,0.996855,0.5,0.75,0.75,0.746835,1,0.996835,0.743671,0.746835,0.5,1,1,1,1,1,0.746835,1,0.75,0.75,0.743671,1,1,0.996835,1,1,0.996855,1,1,1,1,0.75,0.75,1,1,1,1,0.996835,0.993671,1,1,0.75,0.996855,1,0.75,0.996855,1,1,0.746835,1,1,0.996835,1,0.75,1,0.996855,1,0.75,1,1,0.746835,1,1,0.996855,0.746835,0.75,0.746835,1,1,1,0.996855,0.996835,1,1,1,1,1,1,1,1,1,0.5,1]
area_under_roc_curve
0.9242297587771674 [1,1,1,1,0.996855,1,1,1,1,1,1,1,1,1,1,1,1,1,0.496835,0.5,0.993671,1,1,1,1,0.5,0.496855,0.75,1,1,0.996835,1,1,0.996855,1,1,0.493671,0.746835,0.996835,1,1,0.75,1,1,0.5,0.75,1,0.996835,0.996855,0.996855,1,1,0.996835,0.5,1,0.75,1,1,0.5,1,0.996835,0.996835,0.996855,0.996835,1,0.496855,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,0.75,1,1,0.996835,1,1,1,0.5,1,1,1,1,0.75,1,1,0.75,1,1,0.5,1]
area_under_roc_curve
0.9242496616511426 [0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996855,0.984177,0.5,0.75,1,1,1,0.75,0.5,1,1,0.5,1,1,1,0.996855,1,1,1,0.5,0.746835,1,1,1,1,1,1,1,1,1,0.75,0.996855,0.5,0.496855,0.75,0.75,0.996855,1,0.746835,1,1,0.996835,0.990566,1,1,1,1,1,0.496855,0.5,1,1,0.743671,1,1,0.996835,1,1,1,1,0.5,1,1,0.75,1,0.75,0.75,0.75,1,1,0.996855,1,1,1,1,0.996835,1,1,0.996835,1,1,0.996835,1]
area_under_roc_curve
0.9369277923732187 [0.996855,1,1,0.75,1,0.996855,1,1,0.75,1,1,1,1,1,0.996835,1,1,0.493711,0.75,0.496855,0.996835,1,1,1,1,0.5,0.996855,1,1,1,1,1,1,0.996855,1,1,1,0.5,0.75,1,1,1,1,0.996835,1,1,0.996855,1,0.5,1,0.75,0.996835,1,0.75,0.75,1,1,0.5,0.996835,1,0.75,1,1,0.996835,1,0.996855,0.743671,1,0.996835,1,0.996855,1,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.5,1,1,1,1,1,1,0.75,1,1,1,1,1,1,1,0.75,1]
area_under_roc_curve
0.9022967916567153 [0.996855,1,1,1,1,0.996855,1,1,0.75,0.5,0.993711,1,1,0.75,0.996835,1,1,0.5,0.5,0.993711,1,1,1,1,1,0.496855,0.5,0.996835,0.75,1,1,1,1,0.993711,0.75,1,0.746835,1,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.746835,0.75,0.996855,1,1,0.996835,0.75,0.75,1,0.746835,1,0.993711,0.993711,0.5,1,0.75,1,1,0.5,0.746835,0.75,1,0.75,1,1,1,1,1,0.496855,1,0.496855,0.996835,1,1,0.746835,1,1,0.996835,0.996835,1,0.75,1,1,0.996855,0.5,1,1]
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
0.8294512435846823
kappa
0.8294040990403981
kappa
0.892600489615415
kappa
0.8294377763739735
kappa
0.8293906243829233
kappa
0.8294243070362474
kappa
0.8483591991470205
kappa
0.8483591991470205
kappa
0.8736426456071076
kappa
0.8042079501046067
kb_relative_information_score
132.94107512706694
kb_relative_information_score
132.94107512706694
kb_relative_information_score
142.96289915407914
kb_relative_information_score
132.94107512706694
kb_relative_information_score
132.94107512706694
kb_relative_information_score
132.94107512706694
kb_relative_information_score
135.9476223351706
kb_relative_information_score
135.9476223351706
kb_relative_information_score
139.95635194597548
kb_relative_information_score
128.93234551626205
mean_absolute_error
0.003375000000000001
mean_absolute_error
0.003375000000000001
mean_absolute_error
0.002125
mean_absolute_error
0.003375000000000001
mean_absolute_error
0.003375000000000001
mean_absolute_error
0.003375000000000001
mean_absolute_error
0.003000000000000001
mean_absolute_error
0.003000000000000001
mean_absolute_error
0.0025000000000000005
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]
predictive_accuracy
0.83125
predictive_accuracy
0.83125
predictive_accuracy
0.89375
predictive_accuracy
0.83125
predictive_accuracy
0.83125
predictive_accuracy
0.83125
predictive_accuracy
0.85
predictive_accuracy
0.85
predictive_accuracy
0.875
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
0.83125 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.83125 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,1,0.5,0]
recall
0.89375 [1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1]
recall
0.83125 [0,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5]
recall
0.83125 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0,1,0,1,1,0.5,0,1,1,0.5,1,1,0,1,1,1,1,1,1,1,0,0.5,1,0,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1]
recall
0.83125 [0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1]
recall
0.85 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1]
recall
0.85 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
recall
0.875 [1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,0.5,0.5,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1]
recall
0.80625 [1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,1,0.5,1,1,1,0,1,0.5,1,1,0,0.5,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.17045454545454522
relative_absolute_error
0.17045454545454522
relative_absolute_error
0.10732323232323215
relative_absolute_error
0.17045454545454522
relative_absolute_error
0.17045454545454522
relative_absolute_error
0.17045454545454522
relative_absolute_error
0.1515151515151513
relative_absolute_error
0.1515151515151513
relative_absolute_error
0.12626262626262608
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
0.058094750193111264
root_mean_squared_error
0.058094750193111264
root_mean_squared_error
0.046097722286464436
root_mean_squared_error
0.058094750193111264
root_mean_squared_error
0.058094750193111264
root_mean_squared_error
0.058094750193111264
root_mean_squared_error
0.05477225575051662
root_mean_squared_error
0.05477225575051662
root_mean_squared_error
0.05
root_mean_squared_error
0.06224949798994367
root_relative_squared_error
0.5838742081211419
root_relative_squared_error
0.5838742081211419
root_relative_squared_error
0.46329954095214076
root_relative_squared_error
0.5838742081211419
root_relative_squared_error
0.5838742081211419
root_relative_squared_error
0.5838742081211419
root_relative_squared_error
0.5504818825631801
root_relative_squared_error
0.5504818825631801
root_relative_squared_error
0.5025189076296057
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
619.9918309998793
usercpu_time_millis
611.1137479999798
usercpu_time_millis
618.3157770001344
usercpu_time_millis
613.3744869998736
usercpu_time_millis
628.5277420001876
usercpu_time_millis
642.7619660000801
usercpu_time_millis
606.3306499997907
usercpu_time_millis
612.8415420000692
usercpu_time_millis
618.3809159999782
usercpu_time_millis
612.939403999917
usercpu_time_millis_testing
55.176130000063495
usercpu_time_millis_testing
56.81996899988917
usercpu_time_millis_testing
57.696982000152275
usercpu_time_millis_testing
54.47556399985842
usercpu_time_millis_testing
55.942204000075435
usercpu_time_millis_testing
58.2032300001174
usercpu_time_millis_testing
55.17468299990469
usercpu_time_millis_testing
55.91144799996073
usercpu_time_millis_testing
57.93328599997949
usercpu_time_millis_testing
54.95494100000542
usercpu_time_millis_training
564.8157009998158
usercpu_time_millis_training
554.2937790000906
usercpu_time_millis_training
560.6187949999821
usercpu_time_millis_training
558.8989230000152
usercpu_time_millis_training
572.5855380001121
usercpu_time_millis_training
584.5587359999627
usercpu_time_millis_training
551.155966999886
usercpu_time_millis_training
556.9300940001085
usercpu_time_millis_training
560.4476299999988
usercpu_time_millis_training
557.9844629999116