8810768
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)
6785818
axis
0
7660
categorical_features
[]
7660
copy
true
7660
fill_empty
0
7660
missing_values
"NaN"
7660
strategy
"median"
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
22.26982743759295
7708
cache_size
200
7708
class_weight
null
7708
coef0
0.8397611701700962
7708
decision_function_shape
null
7708
degree
5
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gamma
0.00971485253059389
7708
kernel
"sigmoid"
7708
max_iter
-1
7708
probability
true
7708
random_state
11241
7708
shrinking
true
7708
tol
0.013089055713910303
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
18537475
description
https://api.openml.org/data/download/18537475/description.xml
-1
18537476
predictions
https://api.openml.org/data/download/18537476/predictions.arff
area_under_roc_curve
0.9960400883838385 [0.988163,0.999882,1,0.999171,0.996173,0.994476,0.999961,0.998658,0.996015,1,0.999014,1,0.999724,0.978299,0.999092,0.99929,1,0.98761,0.987255,0.970644,0.997751,0.999171,0.994989,1,0.998027,0.979837,0.985598,0.999448,0.999132,0.999961,0.999961,1,1,0.991438,0.999329,0.999132,0.993016,0.985085,0.999842,0.999763,0.998816,0.999527,1,0.999171,0.997988,0.999842,0.998224,0.991872,0.998185,0.995423,0.994555,0.998224,0.997948,0.99128,0.997435,0.984138,1,0.999842,0.9942,0.99491,0.999092,0.99925,0.996686,0.999211,0.999408,0.993056,0.989544,1,0.998974,0.994476,0.994437,0.998856,0.99491,0.998224,0.985085,0.999724,0.999645,0.9884,1,0.999763,0.992977,0.997554,0.998067,0.997396,0.999448,0.999487,1,0.999527,0.999605,0.999921,0.996962,0.998224,0.997633,0.99783,0.998067,0.997672,0.997277,0.99779,0.964765,0.99858]
average_cost
0
f_measure
0.7999698423230379 [0.702703,0.875,1,0.903226,0.903226,0.740741,1,0.967742,0.823529,0.967742,0.8,0.9375,0.9375,0.647059,0.842105,0.903226,0.967742,0.3125,0.387097,0.137931,0.8,0.967742,0.722222,1,0.903226,0.424242,0.378378,0.848485,0.903226,0.933333,0.967742,1,0.967742,0.611111,0.83871,0.8,0.588235,0.333333,0.848485,0.896552,0.787879,0.941176,1,0.875,0.692308,0.9375,0.75,0.709677,0.848485,0.866667,0.8125,0.8,0.866667,0.8,0.709677,0.533333,1,0.903226,0.611111,0.756757,0.75,0.875,0.6875,0.903226,0.875,0.625,0.588235,1,0.875,0.764706,0.758621,0.8,0.625,0.933333,0.787879,0.909091,0.903226,0.5,0.967742,0.969697,0.8,0.848485,0.777778,0.62069,0.857143,0.866667,0.933333,0.909091,0.9375,0.9375,0.733333,0.692308,0.857143,0.875,0.9375,0.787879,0.8,0.736842,0.424242,0.875]
kappa
0.7967171717171717
kb_relative_information_score
965.8080896024339
mean_absolute_error
0.01642563384512133
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
0.8084113022770143 [0.619048,0.875,1,0.933333,0.933333,0.909091,1,1,0.777778,1,0.857143,0.9375,0.9375,0.611111,0.727273,0.933333,1,0.3125,0.4,0.153846,0.857143,1,0.65,1,0.933333,0.411765,0.333333,0.823529,0.933333,1,1,1,1,0.55,0.866667,0.857143,0.555556,0.357143,0.823529,1,0.764706,0.888889,1,0.875,0.9,0.9375,0.75,0.733333,0.823529,0.928571,0.8125,0.857143,0.928571,0.857143,0.733333,0.571429,1,0.933333,0.55,0.666667,0.75,0.875,0.6875,0.933333,0.875,0.625,0.555556,1,0.875,0.722222,0.846154,0.857143,0.625,1,0.764706,0.882353,0.933333,0.45,1,0.941176,0.857143,0.823529,0.7,0.692308,0.789474,0.928571,1,0.882353,0.9375,0.9375,0.785714,0.9,0.789474,0.875,0.9375,0.764706,0.736842,0.636364,0.411765,0.875]
predictive_accuracy
0.79875
prior_entropy
6.6438561897747395
recall
0.79875 [0.8125,0.875,1,0.875,0.875,0.625,1,0.9375,0.875,0.9375,0.75,0.9375,0.9375,0.6875,1,0.875,0.9375,0.3125,0.375,0.125,0.75,0.9375,0.8125,1,0.875,0.4375,0.4375,0.875,0.875,0.875,0.9375,1,0.9375,0.6875,0.8125,0.75,0.625,0.3125,0.875,0.8125,0.8125,1,1,0.875,0.5625,0.9375,0.75,0.6875,0.875,0.8125,0.8125,0.75,0.8125,0.75,0.6875,0.5,1,0.875,0.6875,0.875,0.75,0.875,0.6875,0.875,0.875,0.625,0.625,1,0.875,0.8125,0.6875,0.75,0.625,0.875,0.8125,0.9375,0.875,0.5625,0.9375,1,0.75,0.875,0.875,0.5625,0.9375,0.8125,0.875,0.9375,0.9375,0.9375,0.6875,0.5625,0.9375,0.875,0.9375,0.8125,0.875,0.875,0.4375,0.875]
relative_absolute_error
0.8295774669253181
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08412628279231872
root_relative_squared_error
0.8455009546347059
total_cost
0
area_under_roc_curve
0.9951004099992036 [0.974843,1,1,1,0.993671,1,1,1,0.993671,1,1,1,1,0.955696,1,0.981132,1,1,0.981013,0.974843,0.993711,1,1,1,1,0.990506,0.990506,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.971519,1,1,1,1,0.993711,0.993711,1,1,1,0.996835,1,1,1,1,1,1,0.981013,1,1,0.962264,0.993671,1,1,1,1,1,1,0.96519,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.993671,1,0.984177,1,0.993671,0.908228,1]
area_under_roc_curve
0.9963639937106918 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.924051,1,1,1,0.971519,0.993671,0.987421,1,1,1,1,1,0.981013,0.974684,1,1,1,1,1,1,1,1,1,0.993711,0.987421,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.981132,1,1,1,0.987342,1,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,1,1,1,1,0.987342,1,1,0.981013,1,0.993711,1,0.996835,1,1,1,1,1,0.993671,1,1,0.993671,1,1,1,0.996835,0.984177,0.974843]
area_under_roc_curve
0.9963649888543904 [1,1,1,1,1,0.993671,1,1,0.993671,1,1,1,1,0.993671,0.996835,1,1,0.984177,1,0.936709,1,1,0.987342,1,1,0.974684,0.993671,1,1,1,1,1,1,0.984177,1,1,1,0.993711,1,1,0.993671,0.993711,1,1,0.996835,1,1,0.993711,0.996835,1,1,1,1,1,1,0.993671,1,1,0.981132,0.984177,1,1,1,0.987421,1,0.993671,1,1,1,0.984177,1,0.996835,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,0.993711,0.993671,1,1,1,1,1,1,1,0.949686,1]
area_under_roc_curve
0.9960495283018866 [0.996835,1,1,1,0.987342,0.993671,1,1,0.993671,1,1,1,1,0.984177,1,1,1,0.996835,0.955975,0.955696,1,1,0.996835,1,0.993711,0.974684,1,1,1,1,1,1,1,0.996835,1,1,1,0.974843,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.977848,1,0.981013,1,1,0.993711,1,1,1,1,1,1,0.996835,0.984177,1,1,0.984177,0.974684,1,0.981132,1,0.993711,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.993671,0.974843,0.996835]
area_under_roc_curve
0.996484157312316 [1,1,1,1,1,1,1,0.993671,1,1,0.993671,1,1,0.968553,0.993711,1,1,0.996835,1,0.990506,1,1,1,1,0.993671,0.996835,0.984177,1,1,1,1,1,1,0.977848,1,1,1,0.977848,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.981132,1,1,0.993671,0.993671,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.933544,1,0.993671,1,1,1,1,1,1,0.993671,0.993711,0.993711,1,1,1,1,1,0.996835,1,0.993711,0.974684,0.993711,1,1,0.974843,1]
area_under_roc_curve
0.995138474245681 [0.93038,1,1,0.993671,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.993711,0.962025,0.996835,1,0.990506,1,0.993671,0.981013,0.974684,1,1,1,1,1,1,0.993671,1,0.996835,0.996835,0.977848,1,1,0.996835,1,1,1,1,1,1,0.993671,0.996835,0.996835,1,1,1,1,0.993671,0.984177,1,1,0.996835,1,0.990506,1,0.993711,1,1,0.971519,1,1,1,1,1,1,1,1,0.993671,1,1,0.993671,1,1,0.993711,0.996835,1,0.993671,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,0.924528,1]
area_under_roc_curve
0.9957756149988057 [0.996835,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.987421,0.990506,0.977848,1,1,0.990506,1,1,0.91195,0.949686,0.996835,1,1,0.996835,1,1,0.993711,1,1,0.971519,0.993671,1,1,1,0.996835,1,1,0.974843,1,0.996835,0.987342,1,1,0.987421,1,1,1,0.993671,0.993671,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.96519,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,1,1,1,1,0.943038,1]
area_under_roc_curve
0.9964485809250856 [0.984177,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.981132,0.984177,0.984177,0.993671,1,0.996835,1,0.993671,0.987421,0.993711,1,0.987421,1,1,1,1,1,1,1,0.993671,0.981013,1,1,0.996835,1,1,1,1,1,1,0.990506,1,0.981132,0.955975,0.996835,0.996835,1,1,0.981013,1,1,0.993671,1,1,1,1,1,1,0.968553,0.955975,1,1,0.993671,1,1,1,1,0.996835,1,1,0.974843,1,1,0.987342,1,0.990506,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1]
area_under_roc_curve
0.9970391987102938 [1,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,0.996835,1,1,0.987421,1,0.968553,1,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,0.968354,1,1,1,1,1,0.993671,1,1,0.993711,0.990506,0.993711,1,0.990506,1,1,0.962025,0.996835,0.955975,1,1,0.996835,1,1,1,1,1,1,1,0.984177,1,0.996835,1,1,0.987421,0.981013,1,1,1,1,0.993711,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,1,0.990506,1]
area_under_roc_curve
0.9972750477668975 [0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,0.987421,0.974684,0.981132,1,1,1,1,1,0.993711,0.993711,0.996835,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,0.993671,1,1,1,1,0.981013,1,1,1,0.987342,1,0.993711,0.993711,0.987342,1,1,1,1,1,0.981013,0.987342,1,0.993671,1,1,1,1,1,0.968553,1,0.993711,1,1,1,1,1,1,0.993671,1,1,0.993671,1,1,0.993711,0.996835,1,1]
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
0.7789182787208844
kappa
0.8231206569804169
kappa
0.8420345944238212
kappa
0.7978840991631139
kappa
0.8041383667667036
kappa
0.7536420703541395
kappa
0.8041615667074664
kappa
0.7914938988271532
kappa
0.8168035375868604
kappa
0.7537198563365829
kb_relative_information_score
96.18918792421987
kb_relative_information_score
96.49580057971546
kb_relative_information_score
96.92557907857082
kb_relative_information_score
96.4045237908179
kb_relative_information_score
96.58762189616873
kb_relative_information_score
94.78784523092521
kb_relative_information_score
97.79698532062439
kb_relative_information_score
96.42698087432194
kb_relative_information_score
97.57307778076014
kb_relative_information_score
96.62048712631058
mean_absolute_error
0.016375581281013737
mean_absolute_error
0.016439013340488612
mean_absolute_error
0.01640273704963632
mean_absolute_error
0.016436219640053422
mean_absolute_error
0.016463149514720124
mean_absolute_error
0.016607310937820006
mean_absolute_error
0.01630128028064684
mean_absolute_error
0.016427268344185363
mean_absolute_error
0.016346762102093952
mean_absolute_error
0.016457015960554943
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
0.78125
predictive_accuracy
0.825
predictive_accuracy
0.84375
predictive_accuracy
0.8
predictive_accuracy
0.80625
predictive_accuracy
0.75625
predictive_accuracy
0.80625
predictive_accuracy
0.79375
predictive_accuracy
0.81875
predictive_accuracy
0.75625
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 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,0.5,0,0.5,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1]
recall
0.825 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,0,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,0,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,0]
recall
0.84375 [1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,1]
recall
0.8 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,0.5,1,1,1,0,0,0,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1]
recall
0.80625 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5,1,0.5,0,1,0.5,1,1,1,1,0,1,0.5,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,0,0.5,0,1,1,1,1]
recall
0.75625 [0.5,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0,0.5,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0,1,0,1,1,1,1,0,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5]
recall
0.80625 [1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5,0,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.79375 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,0.5,1,1,1,0.5,0,1,1,0,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1,1,0,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1]
recall
0.81875 [1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,0.5,0,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,0.5,1]
recall
0.75625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,0.5,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,0,0.5,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,0.5,0.5,1,1,1,0.5,0.5,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1]
relative_absolute_error
0.827049559647157
relative_absolute_error
0.830253199014575
relative_absolute_error
0.8284210631129442
relative_absolute_error
0.8301121030329998
relative_absolute_error
0.8314721977131362
relative_absolute_error
0.8387530776676756
relative_absolute_error
0.8232969838710511
relative_absolute_error
0.8296600173830977
relative_absolute_error
0.8255940455602994
relative_absolute_error
0.8311624222502484
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
0.0839931581607379
root_mean_squared_error
0.08410258075773484
root_mean_squared_error
0.08401162020191365
root_mean_squared_error
0.08418687325284897
root_mean_squared_error
0.0842242745365714
root_mean_squared_error
0.08505666253485028
root_mean_squared_error
0.08355631370920083
root_mean_squared_error
0.08410720914646015
root_mean_squared_error
0.08369484201039043
root_mean_squared_error
0.08432061946264333
root_relative_squared_error
0.8441630017458942
root_relative_squared_error
0.8452627402241523
root_relative_squared_error
0.8443485522411792
root_relative_squared_error
0.8461099116754748
root_relative_squared_error
0.8464858087202775
root_relative_squared_error
0.8548516228726595
root_relative_squared_error
0.8397725498140851
root_relative_squared_error
0.8453092572810788
root_relative_squared_error
0.8411648116258766
root_relative_squared_error
0.8474541116603838
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
1575.7990929996595
usercpu_time_millis
1371.701784999459
usercpu_time_millis
1383.819469003356
usercpu_time_millis
1395.908149999741
usercpu_time_millis
1398.4147880037199
usercpu_time_millis
1390.709755003627
usercpu_time_millis
1424.0084730045055
usercpu_time_millis
1416.7559659981634
usercpu_time_millis
1401.7631980022998
usercpu_time_millis
1389.058600005228
usercpu_time_millis_testing
152.4009839995415
usercpu_time_millis_testing
151.97471099963877
usercpu_time_millis_testing
151.26828500069678
usercpu_time_millis_testing
160.6750610008021
usercpu_time_millis_testing
156.78773800027557
usercpu_time_millis_testing
152.42011900409125
usercpu_time_millis_testing
160.57937900040997
usercpu_time_millis_testing
161.00564599764766
usercpu_time_millis_testing
157.08829700452043
usercpu_time_millis_testing
151.94120600062888
usercpu_time_millis_training
1423.398109000118
usercpu_time_millis_training
1219.7270739998203
usercpu_time_millis_training
1232.5511840026593
usercpu_time_millis_training
1235.233088998939
usercpu_time_millis_training
1241.6270500034443
usercpu_time_millis_training
1238.2896359995357
usercpu_time_millis_training
1263.4290940040955
usercpu_time_millis_training
1255.7503200005158
usercpu_time_millis_training
1244.6749009977793
usercpu_time_millis_training
1237.1173940045992