8979105
1
Jan van Rijn
9954
Supervised Classification
8330
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)(2)
6914686
axis
0
7104
categorical_features
[]
7104
copy
true
7104
fill_empty
0
7104
missing_values
"NaN"
7104
strategy
"median"
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strategy_nominal
"most_frequent"
7104
verbose
0
7104
categorical_features
[]
7105
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
7105
handle_unknown
"ignore"
7105
n_values
"auto"
7105
sparse
true
7105
threshold
0.0
7106
C
4.473086749744791
7107
cache_size
200
7107
class_weight
null
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coef0
-0.35993622262715275
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decision_function_shape
null
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degree
5
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gamma
0.0036505689712527617
7107
kernel
"rbf"
7107
max_iter
-1
7107
probability
true
7107
random_state
46965
7107
shrinking
false
7107
tol
3.2883140610524516e-05
7107
verbose
false
7107
copy
true
7713
with_mean
false
7713
with_std
true
7713
openml-pimp
openml-python
Sklearn_0.18.1.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
18873271
description
https://api.openml.org/data/download/18873271/description.xml
-1
18873272
predictions
https://api.openml.org/data/download/18873272/predictions.arff
area_under_roc_curve
0.9966737689393945 [0.9884,0.999961,1,0.999842,0.998343,0.995581,0.999961,0.999961,0.997554,1,0.999448,0.999921,0.999763,0.981731,1,0.999645,1,0.988005,0.989268,0.969894,0.99779,0.999605,0.995423,1,0.999014,0.979324,0.987216,0.999448,0.99929,0.999961,0.999882,1,1,0.991793,0.999369,0.999171,0.994713,0.985677,0.999803,0.999921,0.998737,0.999448,1,0.99929,0.99854,0.999921,0.999014,0.999763,0.998422,0.996449,0.996252,0.998737,0.998303,0.993253,0.997593,0.987019,1,0.999724,0.994831,0.996173,0.998935,0.999448,0.997711,0.999566,0.999408,0.994989,0.989544,1,0.998737,0.994989,0.996449,0.999408,0.995147,0.998895,0.986111,0.999763,0.999684,0.98911,1,1,0.996015,0.997475,0.998777,0.997554,0.999527,0.999803,1,0.999605,0.999566,1,0.99779,0.999527,0.998698,0.998856,0.997869,0.998935,0.99783,0.998343,0.969066,0.998106]
average_cost
0
f_measure
0.8287007861259182 [0.742857,0.967742,1,1,0.933333,0.827586,1,0.967742,0.875,0.967742,0.903226,0.875,0.903226,0.709677,1,0.933333,1,0.307692,0.451613,0.206897,0.866667,0.967742,0.756757,1,0.9375,0.424242,0.470588,0.875,0.903226,0.967742,0.967742,1,0.9375,0.5625,0.8125,0.857143,0.628571,0.432432,0.882353,0.967742,0.705882,0.909091,1,0.848485,0.9375,0.969697,0.827586,0.9375,0.742857,0.866667,0.823529,0.827586,0.903226,0.75,0.774194,0.482759,1,0.9375,0.648649,0.736842,0.83871,0.903226,0.6875,0.9375,0.875,0.647059,0.647059,1,0.866667,0.764706,0.866667,0.827586,0.666667,0.967742,0.742857,0.909091,0.9375,0.529412,0.967742,0.969697,0.83871,0.83871,0.933333,0.645161,0.875,0.903226,0.933333,0.83871,0.967742,0.967742,0.764706,0.827586,0.909091,0.903226,0.967742,0.823529,0.756757,0.833333,0.466667,0.903226]
kappa
0.8257575757575758
kb_relative_information_score
983.2365613855925
mean_absolute_error
0.01624624728227458
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
0.8352046302510695 [0.684211,1,1,1,1,0.923077,1,1,0.875,1,0.933333,0.875,0.933333,0.733333,1,1,1,0.4,0.466667,0.230769,0.928571,1,0.666667,1,0.9375,0.411765,0.444444,0.875,0.933333,1,1,1,0.9375,0.5625,0.8125,0.789474,0.578947,0.380952,0.833333,1,0.666667,0.882353,1,0.823529,0.9375,0.941176,0.923077,0.9375,0.684211,0.928571,0.777778,0.923077,0.933333,0.75,0.8,0.538462,1,0.9375,0.571429,0.636364,0.866667,0.933333,0.6875,0.9375,0.875,0.611111,0.611111,1,0.928571,0.722222,0.928571,0.923077,0.647059,1,0.684211,0.882353,0.9375,0.5,1,0.941176,0.866667,0.866667,1,0.666667,0.875,0.933333,1,0.866667,1,1,0.722222,0.923077,0.882353,0.933333,1,0.777778,0.666667,0.75,0.5,0.933333]
predictive_accuracy
0.8275
prior_entropy
6.6438561897747395
recall
0.8275 [0.8125,0.9375,1,1,0.875,0.75,1,0.9375,0.875,0.9375,0.875,0.875,0.875,0.6875,1,0.875,1,0.25,0.4375,0.1875,0.8125,0.9375,0.875,1,0.9375,0.4375,0.5,0.875,0.875,0.9375,0.9375,1,0.9375,0.5625,0.8125,0.9375,0.6875,0.5,0.9375,0.9375,0.75,0.9375,1,0.875,0.9375,1,0.75,0.9375,0.8125,0.8125,0.875,0.75,0.875,0.75,0.75,0.4375,1,0.9375,0.75,0.875,0.8125,0.875,0.6875,0.9375,0.875,0.6875,0.6875,1,0.8125,0.8125,0.8125,0.75,0.6875,0.9375,0.8125,0.9375,0.9375,0.5625,0.9375,1,0.8125,0.8125,0.875,0.625,0.875,0.875,0.875,0.8125,0.9375,0.9375,0.8125,0.75,0.9375,0.875,0.9375,0.875,0.875,0.9375,0.4375,0.875]
relative_absolute_error
0.8205175395088156
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08305474394803074
root_relative_squared_error
0.83473158404442
total_cost
0
area_under_roc_curve
0.9964839085263912 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.984177,0.974843,0.993711,1,1,1,1,0.987342,0.990506,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,1,1,1,0.993711,0.993711,1,1,1,0.996835,1,1,1,1,1,1,0.990506,1,1,0.962264,0.993671,1,1,1,1,1,1,0.962025,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,0.990506,0.993711,0.993671,0.917722,1]
area_under_roc_curve
0.9967190112252211 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.911392,1,1,1,0.971519,0.993671,0.993711,1,1,1,1,1,0.981013,0.971519,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.981132,1,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.990506,1,1,0.984177,0.996835,1,1,0.996835,1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,0.996835,1,0.974843]
area_under_roc_curve
0.9970364620651221 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,0.933544,1,1,0.996835,1,1,0.968354,0.996835,1,1,1,1,1,1,0.984177,1,1,1,1,1,1,0.996835,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.981132,0.984177,1,1,1,0.993711,1,0.990506,1,1,1,0.987342,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,0.955975,1]
area_under_roc_curve
0.9969190351086699 [1,1,1,1,0.996835,1,1,1,0.993671,1,1,1,1,1,1,1,1,0.993671,0.949686,0.958861,1,1,0.996835,1,0.993711,0.977848,1,1,1,1,1,1,1,0.993671,1,1,1,0.974843,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.981013,1,1,1,1,1,1,1,1,1,0.996835,0.981013,1,1,0.984177,0.984177,1,0.981132,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.996835,0.993711,1]
area_under_roc_curve
0.9966811957646684 [1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,0.955975,1,1,1,0.996835,1,0.990506,1,1,1,1,1,0.996835,0.987342,1,1,1,1,1,1,0.981013,1,1,1,0.971519,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.993671,0.993671,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.93038,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.974684,0.993711,1,1,0.981132,1]
area_under_roc_curve
0.99600723469469 [0.927215,1,1,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.958861,1,1,0.993671,1,0.993671,0.984177,0.971519,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.981013,1,1,0.996835,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,0.996835,0.990506,1,1,0.996835,1,0.993671,1,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.943396,1]
area_under_roc_curve
0.9964077800334366 [0.996835,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.993711,0.990506,0.977848,1,1,0.993671,1,1,0.90566,0.949686,1,1,1,0.996835,1,1,1,1,1,0.971519,0.993671,1,1,1,1,1,1,0.981132,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.971519,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.93038,1]
area_under_roc_curve
0.997080497173792 [0.981013,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.981132,0.993671,0.968354,0.996835,1,0.996835,1,0.996835,0.993711,0.993711,1,0.987421,1,1,1,1,1,1,1,0.990506,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.968553,0.996835,1,1,1,0.984177,1,1,0.993671,1,1,1,1,1,1,0.974843,0.955975,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835]
area_under_roc_curve
0.997749731311201 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,1,0.968553,1,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,0.968354,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.990506,1,1,0.971519,0.996835,0.968553,1,1,1,1,1,1,1,1,1,1,0.987342,1,0.996835,1,1,0.993711,0.977848,1,1,1,1,0.993711,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1]
area_under_roc_curve
0.9975912546771752 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,0.987421,0.971519,0.987421,1,1,1,1,1,0.993711,0.993711,0.996835,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,0.981013,1,1,1,0.993671,1,0.993711,0.993711,0.984177,1,1,1,1,1,0.981013,0.993671,1,0.993671,1,1,1,1,1,0.968553,1,0.987421,1,0.996835,1,1,1,1,0.996835,0.996835,1,1,1,1,0.993711,1,1,1]
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
f_measure
0.8617261904761901 [1,1,1,1,1,1,1,1,1,1,0.666667,1,1,0.666667,1,1,1,0.666667,0.5,0,1,1,0.666667,1,1,0.5,0,1,1,1,1,1,1,1,0.666667,0.666667,0,0.666667,1,1,1,1,1,0.666667,1,1,1,1,0.5,1,0.8,0.666667,0.666667,0.666667,1,0,1,1,0.666667,0.8,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,0.571429,1,1,0.666667,0.666667,1,1,0.5,1,1,0.8,1,1,0.666667,1,1,1,1,1,1,0.8,0.666667,0]
kappa
0.7789357334596557
kappa
0.8610069101678184
kappa
0.8483771618099976
kappa
0.8168469250809189
kappa
0.8420283559101142
kappa
0.8168035375868604
kappa
0.8167890705204138
kappa
0.8230927183699257
kappa
0.8546890424481737
kappa
0.7979080323662917
kb_relative_information_score
98.47830371815951
kb_relative_information_score
97.62855549072525
kb_relative_information_score
98.10099558380689
kb_relative_information_score
98.20083977475238
kb_relative_information_score
98.17732577034343
kb_relative_information_score
97.28510778893335
kb_relative_information_score
100.30496188028634
kb_relative_information_score
97.67518938493671
kb_relative_information_score
99.15160666349166
kb_relative_information_score
98.2336753301571
mean_absolute_error
0.016159381766135926
mean_absolute_error
0.01634451873694534
mean_absolute_error
0.01630268243610223
mean_absolute_error
0.016283945801548835
mean_absolute_error
0.016284174430868822
mean_absolute_error
0.016351049184798024
mean_absolute_error
0.015997611729639725
mean_absolute_error
0.016305078593573667
mean_absolute_error
0.0161713831975974
mean_absolute_error
0.016262646945535697
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
0.8977083333333333 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,0.666667,1,0.5,1,1,0,1,1,0.5,0.666667,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.4,1,1,1,1,1,1,0.5,1,1,0.666667,1,1,1,1,1,1,1,1,1,0.666667,1,0]
predictive_accuracy
0.78125
predictive_accuracy
0.8625
predictive_accuracy
0.85
predictive_accuracy
0.81875
predictive_accuracy
0.84375
predictive_accuracy
0.81875
predictive_accuracy
0.81875
predictive_accuracy
0.825
predictive_accuracy
0.85625
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
0.78125 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,0,1,0,0,0,0,1,1,1,1,0,0.5,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1]
recall
0.8625 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0]
recall
0.85 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1]
recall
0.81875 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1]
recall
0.84375 [1,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1]
recall
0.81875 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,0.5,1,1,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,1]
recall
0.81875 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.825 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,0.5,1,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5]
recall
0.85625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1]
recall
0.8 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1]
relative_absolute_error
0.8161303922290858
relative_absolute_error
0.8254807442901673
relative_absolute_error
0.8233677998031415
relative_absolute_error
0.8224215051287277
relative_absolute_error
0.8224330520640806
relative_absolute_error
0.8258105648887877
relative_absolute_error
0.8079601883656413
relative_absolute_error
0.8234888178572546
relative_absolute_error
0.8167365251311806
relative_absolute_error
0.8213458053300843
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
0.08274843630961712
root_mean_squared_error
0.08342334994099754
root_mean_squared_error
0.08326265011395587
root_mean_squared_error
0.08323216607288657
root_mean_squared_error
0.08317657078729267
root_mean_squared_error
0.08361792213877806
root_mean_squared_error
0.08192111905133745
root_mean_squared_error
0.08331044659589282
root_mean_squared_error
0.0826312809553767
root_mean_squared_error
0.08321031090426131
root_relative_squared_error
0.831653076447336
root_relative_squared_error
0.8384362136630484
root_relative_squared_error
0.8368211196322233
root_relative_squared_error
0.8365147434918577
root_relative_squared_error
0.8359559898481378
root_relative_squared_error
0.8403917378287236
root_relative_squared_error
0.8233382251494595
root_relative_squared_error
0.8373014923500532
root_relative_squared_error
0.8304756208346188
root_relative_squared_error
0.8362950907825853
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
2307.5377539998954
usercpu_time_millis
2106.5578769998865
usercpu_time_millis
2103.406448999749
usercpu_time_millis
2118.5336799999277
usercpu_time_millis
2115.2560759999233
usercpu_time_millis
2110.75055099991
usercpu_time_millis
2129.1872460001287
usercpu_time_millis
2109.573353000087
usercpu_time_millis
2120.4523119999976
usercpu_time_millis
2124.5645899998635
usercpu_time_millis_testing
176.05030899994745
usercpu_time_millis_testing
178.7929909999093
usercpu_time_millis_testing
171.84185199994317
usercpu_time_millis_testing
173.35764499989637
usercpu_time_millis_testing
172.54472399986298
usercpu_time_millis_testing
172.29894999991302
usercpu_time_millis_testing
172.58616100002655
usercpu_time_millis_testing
172.83739499998774
usercpu_time_millis_testing
173.17722000007052
usercpu_time_millis_testing
173.3433059998788
usercpu_time_millis_training
2131.487444999948
usercpu_time_millis_training
1927.7648859999772
usercpu_time_millis_training
1931.5645969998059
usercpu_time_millis_training
1945.1760350000313
usercpu_time_millis_training
1942.7113520000603
usercpu_time_millis_training
1938.451600999997
usercpu_time_millis_training
1956.601085000102
usercpu_time_millis_training
1936.7359580000993
usercpu_time_millis_training
1947.275091999927
usercpu_time_millis_training
1951.2212839999847