10403132
1
Jan van Rijn
9954
Supervised Classification
16360
sklearn.svm.classes.SVC(32)
8240900
C
40.17005239585696
16360
cache_size
200
16360
class_weight
null
16360
coef0
-0.6583864312419172
16360
decision_function_shape
"ovr"
16360
degree
3
16360
gamma
0.3899322854342229
16360
kernel
"sigmoid"
16360
max_iter
-1
16360
probability
false
16360
random_state
54970
16360
shrinking
false
16360
tol
0.0005049151226151174
16360
verbose
false
16360
openml-python
Sklearn_0.21.2.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
21728049
description
https://api.openml.org/data/download/21728049/description.xml
-1
21728050
predictions
https://api.openml.org/data/download/21728050/predictions.arff
area_under_roc_curve
0.7449494949494951 [0.620581,0.649937,1,1,0.779672,0.683081,0.904672,0.810606,0.654356,0.90625,0.686237,0.71654,0.622475,0.530934,0.999684,0.654672,0.936869,0.52178,0.583018,0.557134,0.59375,0.90625,0.684659,1,0.686553,0.498422,0.558081,0.81029,0.810606,0.90625,0.936869,0.96875,0.685606,0.714331,0.618687,0.841225,0.619634,0.556187,0.778409,0.968434,0.745581,0.840593,0.967803,0.870581,0.84375,0.71654,0.810606,0.622475,0.682765,0.809028,0.716856,0.714962,0.622159,0.686553,0.746212,0.557765,0.999684,0.9375,0.684975,0.653093,0.904356,0.811237,0.840278,0.74779,0.904356,0.559343,0.555556,1,0.653725,0.589646,0.655619,0.780934,0.713384,0.778725,0.558081,0.812184,0.779987,0.589331,0.999369,0.905934,0.593119,0.77904,0.716225,0.683712,0.653725,0.811553,1,0.559028,0.905303,0.61774,0.59154,0.967172,0.68529,0.619318,0.870581,0.686237,0.616477,0.561237,0.498422,0.748422]
average_cost
0
f_measure
0.4981878080125678 [0.235294,0.243902,1,1,0.6,0.333333,0.764706,0.625,0.37037,0.896552,0.461538,0.466667,0.285714,0.111111,0.969697,0.384615,0.875,0.042553,0.113208,0.114286,0.315789,0.896552,0.387097,1,0.48,0,0.125,0.606061,0.625,0.896552,0.875,0.967742,0.428571,0.378378,0.2,0.628571,0.216216,0.105263,0.529412,0.9375,0.421053,0.594595,0.882353,0.571429,0.814815,0.466667,0.625,0.285714,0.324324,0.540541,0.482759,0.4,0.275862,0.48,0.444444,0.121212,0.969697,0.933333,0.4,0.322581,0.742857,0.666667,0.578947,0.516129,0.742857,0.142857,0.1,1,0.344828,0.1875,0.434783,0.692308,0.35,0.545455,0.125,0.740741,0.62069,0.181818,0.941176,0.866667,0.285714,0.5625,0.451613,0.352941,0.344828,0.689655,1,0.137931,0.8125,0.186047,0.230769,0.833333,0.413793,0.210526,0.571429,0.461538,0.170213,0.181818,0,0.551724]
kappa
0.4898989898989899
kb_relative_information_score
0.4938978866358879
mean_absolute_error
0.01009999999999984
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
0.528745542993121 [0.222222,0.2,1,1,0.642857,0.3,0.722222,0.625,0.454545,1,0.6,0.5,0.333333,0.5,0.941176,0.5,0.875,0.032258,0.081081,0.105263,1,1,0.4,1,0.666667,0,0.125,0.588235,0.625,1,0.875,1,0.5,0.333333,0.166667,0.578947,0.190476,0.090909,0.5,0.9375,0.363636,0.52381,0.833333,0.461538,1,0.5,0.625,0.333333,0.285714,0.47619,0.538462,0.368421,0.307692,0.666667,0.4,0.117647,0.941176,1,0.428571,0.333333,0.684211,0.714286,0.5,0.533333,0.684211,0.166667,0.083333,1,0.384615,0.1875,0.714286,0.9,0.291667,0.529412,0.125,0.909091,0.692308,0.176471,0.888889,0.928571,0.6,0.5625,0.466667,0.333333,0.384615,0.769231,1,0.153846,0.8125,0.148148,0.3,0.75,0.461538,0.181818,0.461538,0.6,0.129032,0.333333,0,0.615385]
predictive_accuracy
0.495
prior_entropy
6.643856189774611
recall
0.495 [0.25,0.3125,1,1,0.5625,0.375,0.8125,0.625,0.3125,0.8125,0.375,0.4375,0.25,0.0625,1,0.3125,0.875,0.0625,0.1875,0.125,0.1875,0.8125,0.375,1,0.375,0,0.125,0.625,0.625,0.8125,0.875,0.9375,0.375,0.4375,0.25,0.6875,0.25,0.125,0.5625,0.9375,0.5,0.6875,0.9375,0.75,0.6875,0.4375,0.625,0.25,0.375,0.625,0.4375,0.4375,0.25,0.375,0.5,0.125,1,0.875,0.375,0.3125,0.8125,0.625,0.6875,0.5,0.8125,0.125,0.125,1,0.3125,0.1875,0.3125,0.5625,0.4375,0.5625,0.125,0.625,0.5625,0.1875,1,0.8125,0.1875,0.5625,0.4375,0.375,0.3125,0.625,1,0.125,0.8125,0.25,0.1875,0.9375,0.375,0.25,0.75,0.375,0.25,0.125,0,0.5]
relative_absolute_error
0.5101010101010011
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.1004987562112081
root_relative_squared_error
1.0100505037878067
total_cost
0
area_under_roc_curve
0.7545577581402754 [0.490566,0.5,1,1,0.996835,0.987421,0.496835,0.996855,0.5,1,0.75,0.5,0.5,0.5,1,0.996855,0.996835,0.5,0.5,0.981132,0.5,1,0.990566,1,0.496855,0.5,0.5,0.996835,0.75,0.5,1,1,0.5,0.5,0.5,0.993711,0.996855,0.987421,0.993711,1,0.990566,0.993711,0.996835,0.996835,1,0.75,1,0.993711,0.75,0.743671,1,0.5,0.990566,0.5,0.993711,0.984277,1,1,0.5,0.75,0.996855,0.993711,1,0.75,1,0.5,0.5,1,0.993711,0.487421,0.75,1,1,0.5,0.487421,0.75,0.5,0.5,1,1,0.5,0.75,1,0.993711,0.5,0.75,1,0.5,0.75,0.987421,0.5,1,0.5,0.5,0.75,0.5,0.984277,0.5,0.5,0.996855]
area_under_roc_curve
0.7544781466443752 [0.990566,0.5,1,1,1,0.993711,0.75,0.993711,0.5,1,0.5,0.5,0.5,0.5,1,0.996855,1,0.5,0.5,0.981132,1,0.5,0.493711,1,1,0.5,0.5,0.75,1,1,0.996835,1,0.5,0.5,0.5,0.990566,0.993711,0.993711,0.996855,1,0.984277,0.993711,1,0.996835,0.75,0.5,0.996855,0.990566,0.496835,1,1,0.746835,0.987421,0.75,0.996855,0.487421,1,1,0.996855,0.5,0.996855,0.996855,0.996835,0.496835,0.993711,0.5,0.5,1,0.496855,0.993711,0.743671,1,1,0.496835,0.493711,0.496835,0.5,0.493671,1,0.75,0.5,0.75,0.990566,1,0.5,1,1,0.5,0.75,0.984277,0.5,1,0.5,0.5,1,0.5,0.990566,0.5,0.5,0.996855]
area_under_roc_curve
0.7389140991959237 [0.5,0.5,1,1,0.5,0.996835,1,0.5,1,1,0.75,0.5,0.993711,0.5,1,0.5,0.75,0.5,0.484277,0.5,0.5,0.5,0.5,1,0.996855,0.5,0.5,0.993711,0.75,1,1,1,0.75,0.5,0.981132,0.996855,0.974843,0.481132,0.993711,0.75,0.5,0.996855,1,0.984277,1,0.996855,0.5,0.993711,0.5,1,0.5,0.996855,1,0.5,0.984277,0.5,1,1,0.990566,0.5,0.996855,0.496855,0.75,0.490566,0.746835,0.5,0.5,1,0.993711,0.5,0.5,0.996835,0.981132,1,0.987421,0.75,0.5,0.5,1,1,0.496855,0.75,0.5,0.5,0.993711,1,1,0.5,0.996835,0.981132,0.75,0.996855,1,0.5,1,1,0.5,0.5,0.496855,0.75]
area_under_roc_curve
0.7325451795239231 [0.496835,0.5,1,1,0.75,0.496835,1,0.5,0.746835,0.75,0.75,0.5,0.996855,0.5,1,0.5,0.996835,0.5,0.987421,0.5,0.5,1,0.746835,1,0.5,0.5,0.5,0.990566,0.5,1,1,1,0.5,0.5,0.984277,0.993711,0.981132,0.474843,0.993711,1,1,0.984277,1,0.993711,1,0.990566,0.5,0.996855,0.5,1,0.5,0.993711,0.993711,0.5,0.996855,0.5,1,1,0.990566,0.5,0.996855,0.5,0.996835,0.5,0.746835,0.5,0.5,1,0.493711,0.5,0.5,0.75,0.487421,0.75,0.987421,1,0.75,0.5,0.996835,0.75,1,0.75,0.5,0.5,0.990566,1,1,0.5,1,0.974843,0.5,1,1,0.5,1,0.993711,0.5,0.5,0.490566,0.5]
area_under_roc_curve
0.7482485470901996 [0.5,0.993711,1,1,1,0.5,0.996855,0.746835,0.496855,0.75,0.5,1,0.993711,0.496855,0.996855,0.5,1,0.5,0.959119,0.5,0.5,1,0.75,1,0.5,0.5,0.5,1,0.996855,0.75,0.996855,0.5,0.746835,0.996835,0.990566,1,0.5,0.5,0.75,1,0.75,0.5,1,1,1,0.993711,1,0.5,0.75,0.5,0.5,0.490566,0.5,0.496855,0.743671,0.5,0.996855,1,0.5,0.993711,1,1,0.990566,0.996855,0.75,0.5,0.474843,1,0.5,0.5,0.5,0.75,0.990566,0.996855,0.5,1,0.996835,0.5,1,1,0.496855,0.75,0.5,0.743671,0.993711,0.996855,1,0.996855,1,0.5,0.493711,0.746835,0.981132,0.990566,0.75,0.996855,0.5,0.990566,0.5,0.75]
area_under_roc_curve
0.7419592389140991 [0.5,0.990566,1,1,0.993711,0.5,0.993711,1,1,0.75,0.5,0.75,0.990566,0.5,1,0.5,0.75,0.5,0.962264,0.5,0.5,0.75,0.75,1,0.5,0.5,0.5,0.496855,1,1,1,1,0.496835,0.743671,0.981132,0.75,0.5,0.5,0.5,1,0.75,0.5,1,0.993711,1,0.996855,1,0.5,0.5,0.5,1,0.987421,0.5,1,0.746835,0.5,1,0.75,0.5,0.996855,1,1,0.993711,0.993711,1,0.5,0.968553,1,0.5,0.5,0.5,0.5,0.987421,0.987421,0.5,1,1,0.5,1,1,0.5,0.75,0.5,0.990506,0.996855,0.996855,1,0.993711,0.996835,0.5,0.493711,0.996835,0.996855,0.987421,0.5,0.996855,0.5,0.996855,0.496855,0.5]
area_under_roc_curve
0.7545776610142502 [1,0.981132,1,1,0.996855,0.5,1,1,0.993711,1,0.996855,0.990566,0.5,1,1,0.75,1,0.981132,0.5,0.5,0.5,1,0.5,1,0.5,0.490566,0.474843,0.5,0.993711,1,0.75,1,0.996855,0.987421,0.5,0.75,0.5,0.5,0.993671,1,0.746835,1,1,0.5,0.5,0.5,0.5,0.5,0.984277,0.996855,0.493711,0.75,0.5,0.996855,0.75,0.5,1,1,0.5,0.990566,0.993671,0.75,0.993711,1,1,0.490566,0.493711,1,0.75,0.5,1,0.75,0.5,1,0.5,1,1,0.493711,1,1,0.5,0.993711,0.996835,0.496835,0.5,0.75,1,0.487421,0.5,0.5,0.996855,1,0.5,0.984277,0.996855,0.5,0.5,0.5,0.5,1]
area_under_roc_curve
0.7451636016240744 [0.5,0.974843,1,1,0.996855,0.5,1,0.75,0.493711,1,0.5,0.993711,0.5,0.5,1,0.5,1,0.465409,0.5,0.5,0.75,1,0.496835,1,0.5,0.493711,0.996855,0.5,0.490566,1,0.75,1,1,0.993711,0.5,0.75,0.5,0.5,1,0.996855,0.493671,1,0.993671,0.5,1,0.5,1,0.5,0.987421,0.496855,0.487421,0.5,0.5,0.996855,0.75,0.5,1,1,1,0.987421,1,1,0.996855,0.75,1,0.487421,0.993711,1,0.5,0.5,1,0.75,0.5,1,0.5,1,1,0.981132,1,1,0.5,0.996855,0.75,0.75,0.5,0.5,1,0.487421,1,0.5,0.493711,0.996835,1,0.981132,0.993711,1,0.5,0.5,0.5,0.5]
area_under_roc_curve
0.7388742934479737 [0.481132,0.746835,1,1,0.5,0.990566,0.996835,0.996855,0.5,1,0.993711,0.993711,0.5,0.5,1,0.993711,1,0.484277,0.5,0.487421,0.75,1,0.996855,1,1,0.5,0.990566,1,1,1,1,1,0.993711,0.996855,0.5,1,0.5,0.5,0.5,1,0.5,0.75,0.75,0.996835,0.75,0.5,0.996855,0.5,0.993711,0.987421,0.75,0.746835,0.5,0.75,0.5,0.490566,1,0.5,0.5,0.5,0.5,0.5,0.5,0.75,1,0.993711,0.5,1,0.746835,0.990566,1,0.5,0.5,0.746835,0.5,0.75,0.996855,0.993711,0.996855,0.75,0.75,0.990566,0.993711,0.490566,0.75,0.75,1,0.5,0.996855,0.5,0.5,1,0.5,0.5,0.974843,0.5,0.974843,0.5,0.5,0.996855]
area_under_roc_curve
0.7482485470901994 [0.996855,0.5,1,1,0.5,0.990566,1,0.996855,0.5,1,0.996855,1,0.5,0.5,1,0.996855,1,0.474843,0.5,0.496855,0.5,1,0.996855,1,0.996835,0.5,0.493711,1,0.75,0.5,1,1,0.996855,0.987421,0.5,0.5,0.5,0.5,0.5,1,0.990566,1,1,0.993671,0.5,1,0.987421,0.5,0.990566,0.990566,0.75,0.75,0.5,0.75,0.5,0.990566,1,1,0.746835,0.5,0.75,0.75,0.496835,0.75,0.993711,0.996855,0.5,1,0.75,0.987421,0.5,1,0.5,0.746835,0.5,0.75,0.993711,0.993711,1,0.996835,0.75,0.496855,0.993711,0.496855,0.5,0.746835,1,0.5,1,0.5,0.75,0.996835,0.5,0.5,0.990566,0.5,0.965409,0.5,0.5,0.993711]
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
0.5083323484221723
kappa
0.5082936054528978
kappa
0.47698003229490765
kappa
0.464334948205916
kappa
0.49568574918245933
kappa
0.48322041909563573
kappa
0.5082936054528978
kappa
0.4894220541307174
kappa
0.4769182290846069
kappa
0.49572548556120233
kb_relative_information_score
0.5114360786831537
kb_relative_information_score
0.5114360786831539
kb_relative_information_score
0.4801178785987405
kb_relative_information_score
0.4675905985649753
kb_relative_information_score
0.4989087986493888
kb_relative_information_score
0.4863815186156235
kb_relative_information_score
0.5114360786831537
kb_relative_information_score
0.49264515863250574
kb_relative_information_score
0.48011787859874067
kb_relative_information_score
0.49890879864938875
mean_absolute_error
0.009750000000000005
mean_absolute_error
0.009750000000000005
mean_absolute_error
0.010375000000000006
mean_absolute_error
0.010625000000000006
mean_absolute_error
0.010000000000000005
mean_absolute_error
0.010250000000000006
mean_absolute_error
0.009750000000000005
mean_absolute_error
0.010125000000000006
mean_absolute_error
0.010375000000000006
mean_absolute_error
0.010000000000000005
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
0.5125
predictive_accuracy
0.5125
predictive_accuracy
0.48125
predictive_accuracy
0.46875
predictive_accuracy
0.5
predictive_accuracy
0.4875
predictive_accuracy
0.5125
predictive_accuracy
0.49375
predictive_accuracy
0.48125
predictive_accuracy
0.5
prior_entropy
6.643856189774732
prior_entropy
6.643856189774732
prior_entropy
6.643856189774732
prior_entropy
6.643856189774732
prior_entropy
6.643856189774732
prior_entropy
6.643856189774732
prior_entropy
6.643856189774732
prior_entropy
6.643856189774732
prior_entropy
6.643856189774732
prior_entropy
6.643856189774732
recall
0.5125 [0,0,1,1,1,1,0,1,0,1,0.5,0,0,0,1,1,1,0,0,1,0,1,1,1,0,0,0,1,0.5,0,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,0,1,0,1,1,1,1,0,0.5,1,1,1,0.5,1,0,0,1,1,0,0.5,1,1,0,0,0.5,0,0,1,1,0,0.5,1,1,0,0.5,1,0,0.5,1,0,1,0,0,0.5,0,1,0,0,1]
recall
0.5125 [1,0,1,1,1,1,0.5,1,0,1,0,0,0,0,1,1,1,0,0,1,1,0,0,1,1,0,0,0.5,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,0,1,1,1,0,1,0,0,1,0,1,0.5,1,1,0,0,0,0,0,1,0.5,0,0.5,1,1,0,1,1,0,0.5,1,0,1,0,0,1,0,1,0,0,1]
recall
0.48125 [0,0,1,1,0,1,1,0,1,1,0.5,0,1,0,1,0,0.5,0,0,0,0,0,0,1,1,0,0,1,0.5,1,1,1,0.5,0,1,1,1,0,1,0.5,0,1,1,1,1,1,0,1,0,1,0,1,1,0,1,0,1,1,1,0,1,0,0.5,0,0.5,0,0,1,1,0,0,1,1,1,1,0.5,0,0,1,1,0,0.5,0,0,1,1,1,0,1,1,0.5,1,1,0,1,1,0,0,0,0.5]
recall
0.46875 [0,0,1,1,0.5,0,1,0,0.5,0.5,0.5,0,1,0,1,0,1,0,1,0,0,1,0.5,1,0,0,0,1,0,1,1,1,0,0,1,1,1,0,1,1,1,1,1,1,1,1,0,1,0,1,0,1,1,0,1,0,1,1,1,0,1,0,1,0,0.5,0,0,1,0,0,0,0.5,0,0.5,1,1,0.5,0,1,0.5,1,0.5,0,0,1,1,1,0,1,1,0,1,1,0,1,1,0,0,0,0]
recall
0.5 [0,1,1,1,1,0,1,0.5,0,0.5,0,1,1,0,1,0,1,0,1,0,0,1,0.5,1,0,0,0,1,1,0.5,1,0,0.5,1,1,1,0,0,0.5,1,0.5,0,1,1,1,1,1,0,0.5,0,0,0,0,0,0.5,0,1,1,0,1,1,1,1,1,0.5,0,0,1,0,0,0,0.5,1,1,0,1,1,0,1,1,0,0.5,0,0.5,1,1,1,1,1,0,0,0.5,1,1,0.5,1,0,1,0,0.5]
recall
0.4875 [0,1,1,1,1,0,1,1,1,0.5,0,0.5,1,0,1,0,0.5,0,1,0,0,0.5,0.5,1,0,0,0,0,1,1,1,1,0,0.5,1,0.5,0,0,0,1,0.5,0,1,1,1,1,1,0,0,0,1,1,0,1,0.5,0,1,0.5,0,1,1,1,1,1,1,0,1,1,0,0,0,0,1,1,0,1,1,0,1,1,0,0.5,0,1,1,1,1,1,1,0,0,1,1,1,0,1,0,1,0,0]
recall
0.5125 [1,1,1,1,1,0,1,1,1,1,1,1,0,1,1,0.5,1,1,0,0,0,1,0,1,0,0,0,0,1,1,0.5,1,1,1,0,0.5,0,0,1,1,0.5,1,1,0,0,0,0,0,1,1,0,0.5,0,1,0.5,0,1,1,0,1,1,0.5,1,1,1,0,0,1,0.5,0,1,0.5,0,1,0,1,1,0,1,1,0,1,1,0,0,0.5,1,0,0,0,1,1,0,1,1,0,0,0,0,1]
recall
0.49375 [0,1,1,1,1,0,1,0.5,0,1,0,1,0,0,1,0,1,0,0,0,0.5,1,0,1,0,0,1,0,0,1,0.5,1,1,1,0,0.5,0,0,1,1,0,1,1,0,1,0,1,0,1,0,0,0,0,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,1,1,0,0,1,0.5,0,1,0,1,1,1,1,1,0,1,0.5,0.5,0,0,1,0,1,0,0,1,1,1,1,1,0,0,0,0]
recall
0.48125 [0,0.5,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,0,0,0,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,0,0,0,1,0,0.5,0.5,1,0.5,0,1,0,1,1,0.5,0.5,0,0.5,0,0,1,0,0,0,0,0,0,0.5,1,1,0,1,0.5,1,1,0,0,0.5,0,0.5,1,1,1,0.5,0.5,1,1,0,0.5,0.5,1,0,1,0,0,1,0,0,1,0,1,0,0,1]
recall
0.5 [1,0,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,0,0,0,0,1,1,1,1,0,0,1,0.5,0,1,1,1,1,0,0,0,0,0,1,1,1,1,1,0,1,1,0,1,1,0.5,0.5,0,0.5,0,1,1,1,0.5,0,0.5,0.5,0,0.5,1,1,0,1,0.5,1,0,1,0,0.5,0,0.5,1,1,1,1,0.5,0,1,0,0,0.5,1,0,1,0,0.5,1,0,0,1,0,1,0,0,1]
relative_absolute_error
0.4924242424242419
relative_absolute_error
0.4924242424242419
relative_absolute_error
0.5239898989898983
relative_absolute_error
0.5366161616161611
relative_absolute_error
0.5050505050505044
relative_absolute_error
0.5176767676767671
relative_absolute_error
0.4924242424242419
relative_absolute_error
0.5113636363636358
relative_absolute_error
0.5239898989898983
relative_absolute_error
0.5050505050505044
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
0.09874208829065752
root_mean_squared_error
0.09874208829065752
root_mean_squared_error
0.10185774393731684
root_mean_squared_error
0.10307764064044155
root_mean_squared_error
0.10000000000000003
root_mean_squared_error
0.10124228365658296
root_mean_squared_error
0.09874208829065752
root_mean_squared_error
0.10062305898749056
root_mean_squared_error
0.10185774393731684
root_mean_squared_error
0.10000000000000003
root_relative_squared_error
0.992395326897746
root_relative_squared_error
0.992395326897746
root_relative_squared_error
1.0237088443399311
root_relative_squared_error
1.0359692675134347
root_relative_squared_error
1.0050378152592119
root_relative_squared_error
1.017523235780655
root_relative_squared_error
0.992395326897746
root_relative_squared_error
1.011299793694863
root_relative_squared_error
1.0237088443399311
root_relative_squared_error
1.0050378152592119
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
514.4666569999572
usercpu_time_millis
510.18862400042053
usercpu_time_millis
511.71848700005285
usercpu_time_millis
514.2396420001205
usercpu_time_millis
512.9506799999035
usercpu_time_millis
512.7693790000194
usercpu_time_millis
517.0287340001778
usercpu_time_millis
513.5843419998309
usercpu_time_millis
512.4227539995445
usercpu_time_millis
511.5790360000574
usercpu_time_millis_testing
67.66712399985408
usercpu_time_millis_testing
66.4495750002061
usercpu_time_millis_testing
66.61417499981326
usercpu_time_millis_testing
67.22555699980148
usercpu_time_millis_testing
66.64748100001816
usercpu_time_millis_testing
66.61294400009865
usercpu_time_millis_testing
67.03585400009615
usercpu_time_millis_testing
67.08545699984825
usercpu_time_millis_testing
66.58649999963018
usercpu_time_millis_testing
66.6687699999784
usercpu_time_millis_training
446.7995330001031
usercpu_time_millis_training
443.73904900021444
usercpu_time_millis_training
445.1043120002396
usercpu_time_millis_training
447.014085000319
usercpu_time_millis_training
446.30319899988535
usercpu_time_millis_training
446.15643499992075
usercpu_time_millis_training
449.99288000008164
usercpu_time_millis_training
446.4988849999827
usercpu_time_millis_training
445.83625399991433
usercpu_time_millis_training
444.910266000079
wall_clock_time_millis
514.5175457000732
wall_clock_time_millis
510.2207660675049
wall_clock_time_millis
511.75808906555176
wall_clock_time_millis
514.2722129821777
wall_clock_time_millis
512.993574142456
wall_clock_time_millis
512.8209590911865
wall_clock_time_millis
517.0786380767822
wall_clock_time_millis
513.6270523071289
wall_clock_time_millis
512.4549865722656
wall_clock_time_millis
511.6159915924072
wall_clock_time_millis_testing
67.67749786376953
wall_clock_time_millis_testing
66.45941734313965
wall_clock_time_millis_testing
66.62321090698242
wall_clock_time_millis_testing
67.23499298095703
wall_clock_time_millis_testing
66.65778160095215
wall_clock_time_millis_testing
66.6346549987793
wall_clock_time_millis_testing
67.04998016357422
wall_clock_time_millis_testing
67.09575653076172
wall_clock_time_millis_testing
66.59674644470215
wall_clock_time_millis_testing
66.67757034301758
wall_clock_time_millis_training
446.8400478363037
wall_clock_time_millis_training
443.76134872436523
wall_clock_time_millis_training
445.13487815856934
wall_clock_time_millis_training
447.0372200012207
wall_clock_time_millis_training
446.3357925415039
wall_clock_time_millis_training
446.1863040924072
wall_clock_time_millis_training
450.028657913208
wall_clock_time_millis_training
446.5312957763672
wall_clock_time_millis_training
445.8582401275635
wall_clock_time_millis_training
444.93842124938965