8699601
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)
6677020
axis
0
7660
categorical_features
[]
7660
copy
true
7660
fill_empty
0
7660
missing_values
"NaN"
7660
strategy
"mean"
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
17.084223979669474
7708
cache_size
200
7708
class_weight
null
7708
coef0
-0.9637266757206799
7708
decision_function_shape
null
7708
degree
4
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gamma
0.0002731136995389932
7708
kernel
"rbf"
7708
max_iter
-1
7708
probability
true
7708
random_state
551
7708
shrinking
true
7708
tol
0.01176303774148489
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
18316125
description
https://api.openml.org/data/download/18316125/description.xml
-1
18316126
predictions
https://api.openml.org/data/download/18316126/predictions.arff
area_under_roc_curve
0.9942475536616157 [0.986348,0.999961,1,1,0.998106,0.993766,0.999921,0.999921,0.995226,1,0.999053,0.999684,0.997751,0.978812,0.999842,0.999842,1,0.974945,0.979324,0.960464,0.997041,0.999329,0.98982,0.999842,0.997554,0.968119,0.976089,0.999132,0.998856,0.999842,0.999803,0.999961,1,0.986466,0.998737,0.998264,0.982126,0.96441,0.999684,0.999842,0.99779,0.998856,1,0.999132,0.997869,0.999684,0.998816,0.998343,0.998856,0.994752,0.991438,0.997396,0.997435,0.989031,0.996291,0.969973,1,0.999684,0.991004,0.986545,0.997554,0.999369,0.996962,0.999369,0.999369,0.992148,0.974747,1,0.99854,0.992819,0.991201,0.998461,0.992109,0.998422,0.981376,0.999684,0.99925,0.985243,0.999803,0.999921,0.994949,0.997238,0.998343,0.995778,0.998303,0.999684,1,0.999487,0.99854,0.999763,0.992266,0.999566,0.997988,0.998224,0.99779,0.998224,0.991635,0.998224,0.955374,0.996252]
average_cost
0
f_measure
0.759712180880492 [0.578947,0.967742,1,1,0.933333,0.733333,1,0.967742,0.774194,0.896552,0.8,0.9375,0.903226,0.645161,0.967742,0.933333,1,0.137931,0.30303,0.060606,0.666667,0.967742,0.611111,1,0.789474,0.266667,0.222222,0.857143,0.875,1,0.9375,0.967742,0.9375,0.387097,0.8,0.736842,0.296296,0.090909,0.882353,0.933333,0.648649,0.875,0.967742,0.888889,0.9375,0.909091,0.827586,0.827586,0.8,0.758621,0.666667,0.8,0.83871,0.62069,0.647059,0.129032,1,0.9375,0.583333,0.625,0.8125,0.9375,0.727273,0.875,0.875,0.666667,0.222222,0.969697,0.823529,0.6,0.709677,0.827586,0.4,0.967742,0.777778,0.909091,0.866667,0.4,0.9375,0.9375,0.909091,0.83871,0.882353,0.210526,0.823529,0.903226,0.933333,0.866667,0.909091,0.967742,0.533333,0.827586,0.848485,0.9375,0.967742,0.8,0.777778,0.769231,0.193548,0.774194]
kappa
0.7607323232323233
kb_relative_information_score
944.0582662268806
mean_absolute_error
0.016493592517163393
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
0.7687889740884595 [0.5,1,1,1,1,0.785714,1,1,0.8,1,0.857143,0.9375,0.933333,0.666667,1,1,1,0.153846,0.294118,0.058824,0.6,1,0.55,1,0.681818,0.285714,0.2,0.789474,0.875,1,0.9375,1,0.9375,0.4,0.857143,0.636364,0.363636,0.166667,0.833333,1,0.571429,0.875,1,0.8,0.9375,0.882353,0.923077,0.923077,0.736842,0.846154,0.714286,0.857143,0.866667,0.692308,0.611111,0.133333,1,0.9375,0.4375,0.625,0.8125,0.9375,0.705882,0.875,0.875,0.647059,0.272727,0.941176,0.777778,0.642857,0.733333,0.923077,0.428571,1,0.7,0.882353,0.928571,0.333333,0.9375,0.9375,0.882353,0.866667,0.833333,0.666667,0.777778,0.933333,1,0.928571,0.882353,1,0.571429,0.923077,0.823529,0.9375,1,0.736842,0.7,0.652174,0.2,0.8]
predictive_accuracy
0.763125
prior_entropy
6.6438561897747395
recall
0.763125 [0.6875,0.9375,1,1,0.875,0.6875,1,0.9375,0.75,0.8125,0.75,0.9375,0.875,0.625,0.9375,0.875,1,0.125,0.3125,0.0625,0.75,0.9375,0.6875,1,0.9375,0.25,0.25,0.9375,0.875,1,0.9375,0.9375,0.9375,0.375,0.75,0.875,0.25,0.0625,0.9375,0.875,0.75,0.875,0.9375,1,0.9375,0.9375,0.75,0.75,0.875,0.6875,0.625,0.75,0.8125,0.5625,0.6875,0.125,1,0.9375,0.875,0.625,0.8125,0.9375,0.75,0.875,0.875,0.6875,0.1875,1,0.875,0.5625,0.6875,0.75,0.375,0.9375,0.875,0.9375,0.8125,0.5,0.9375,0.9375,0.9375,0.8125,0.9375,0.125,0.875,0.875,0.875,0.8125,0.9375,0.9375,0.5,0.75,0.875,0.9375,0.9375,0.875,0.875,0.9375,0.1875,0.75]
relative_absolute_error
0.8330097230890587
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08461811912143777
root_relative_squared_error
0.8504440957315349
total_cost
0
area_under_roc_curve
0.9944689913223469 [0.974843,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,0.971519,1,0.993711,1,0.993671,0.974684,0.968553,0.993711,1,1,1,1,0.984177,0.981013,1,1,0.993711,1,1,1,0.987342,1,0.993711,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,1,0.977848,0.996835,1,1,0.993671,0.993711,1,1,1,0.987421,0.990506,1,1,1,1,1,1,0.984177,1,1,0.955975,0.981013,1,0.996835,1,1,1,1,0.962025,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993671,1,1,0.996835,1,0.987342,0.981132,0.996835,0.908228,1]
area_under_roc_curve
0.9944276928588488 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.914557,1,1,1,0.977848,0.987342,0.993711,1,1,0.987421,1,1,0.971519,0.96519,1,1,1,1,1,1,0.993671,0.990506,0.993711,0.987421,0.981132,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993671,0.996835,1,1,1,0.962264,1,1,1,0.971519,1,1,1,1,1,0.996835,0.984177,1,1,1,0.990506,1,1,1,1,1,1,0.974684,1,1,0.981013,0.996835,1,1,0.993671,1,1,1,1,1,0.993671,1,1,0.987342,1,1,0.987421,0.996835,0.968354,0.974843]
area_under_roc_curve
0.9949426797229517 [0.993671,1,1,1,1,0.993671,1,1,0.990506,1,1,1,0.993711,0.996835,1,1,1,0.946203,1,0.927215,1,1,0.987342,1,1,0.977848,0.981013,1,1,1,1,1,1,0.981013,1,1,0.993711,1,1,1,0.996835,0.993711,1,1,1,1,1,1,0.996835,1,0.993671,1,1,1,1,0.996835,1,1,0.981132,0.968354,0.993711,1,1,0.974843,1,0.984177,0.993671,1,1,0.993671,0.993671,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.984177,1,1,1,1,1,1,1,0.924528,1]
area_under_roc_curve
0.9950628433245761 [1,1,1,1,0.996835,0.996835,1,1,0.993671,1,1,1,1,1,1,1,1,0.987342,0.930818,0.993671,1,1,0.993671,1,0.987421,0.962025,0.952532,1,1,1,1,1,1,0.990506,1,1,0.987421,0.987421,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,0.971519,1,1,0.987421,0.987342,1,1,1,1,1,1,0.993671,1,1,0.984177,0.968354,0.996835,0.974843,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.977848,0.993671,0.962264,0.993671]
area_under_roc_curve
0.9932815361038134 [0.993671,1,1,1,1,1,1,1,1,1,0.993671,1,0.993711,0.962264,1,1,1,0.990506,0.974843,0.936709,1,1,0.990506,1,0.996835,0.971519,0.974684,1,1,1,1,1,1,0.974684,1,1,0.977848,0.93038,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.993671,0.987342,1,0.996835,0.996835,1,0.993671,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,1,0.901899,1,0.993671,0.990506,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.974684,0.993711,1,1,0.949686,1]
area_under_roc_curve
0.9930422040442639 [0.927215,1,1,1,1,0.981013,1,1,1,1,1,1,0.993711,1,1,1,1,0.955696,0.987421,0.905063,0.996835,1,0.990506,1,0.993671,0.968354,0.981013,1,1,1,1,1,1,0.993671,1,0.993671,0.993671,0.962025,1,1,0.996835,1,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,1,1,0.987342,0.943038,1,1,0.993671,1,0.990506,1,0.993711,1,1,0.984177,1,1,0.996835,1,1,1,1,1,0.993671,1,1,0.990506,1,1,1,0.996835,1,0.993671,1,1,1,1,1,1,0.987421,1,1,1,1,1,1,1,0.930818,0.996835]
area_under_roc_curve
0.9938778759652893 [0.990506,1,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,0.993711,0.993671,0.943038,0.996835,1,0.977848,1,1,0.893082,0.949686,0.993671,1,1,0.996835,1,1,0.981132,1,1,0.96519,0.949367,1,1,1,0.996835,1,1,0.981132,0.996835,0.996835,0.996835,1,1,1,1,1,1,0.993671,0.984177,1,1,0.987342,1,1,1,1,0.996835,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.974684,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,0.987342,1,0.927215,1]
area_under_roc_curve
0.994515016718414 [1,1,1,1,1,1,1,1,0.993711,1,1,1,0.990506,1,1,1,1,0.930818,0.974684,0.993671,0.993671,1,0.993671,1,0.996835,0.987421,0.993711,1,0.981132,0.996835,1,1,1,0.987421,0.996835,1,0.977848,0.943038,1,1,0.987342,1,1,1,1,1,1,0.993671,1,0.981132,0.943396,0.996835,0.996835,1,1,0.96519,1,1,0.987342,1,1,1,1,1,1,0.968553,0.924528,1,1,0.990506,1,1,1,1,0.996835,1,1,0.974843,1,1,0.990506,1,0.987342,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.996835,0.996835,1,0.993671,0.993671]
area_under_roc_curve
0.9958141768171321 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,0.990506,0.974843,0.993671,1,0.974843,1,1,1,0.993711,1,1,1,1,1,1,0.987421,1,1,0.987342,0.949367,1,1,0.993711,1,1,0.990506,1,1,1,0.993671,1,0.993711,0.984177,1,1,0.962025,0.996835,0.930818,1,1,0.996835,0.993671,1,1,1,1,1,0.993711,0.971519,1,0.996835,1,1,0.993711,0.974684,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.993671,1,1,1,1,0.993671,0.993711,1,0.990506,1]
area_under_roc_curve
0.9964856500278637 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,0.962264,0.984177,0.974843,0.996835,1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,0.993671,0.996835,0.974684,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.990506,1,0.993711,1,1,0.981013,0.993671,1,1,0.990506,1,0.993711,0.993711,0.971519,1,1,1,1,1,0.981013,0.990506,1,0.993671,1,0.993711,1,1,1,0.968553,1,0.987421,1,0.996835,1,1,1,1,0.987342,1,1,1,1,1,0.981132,1,0.996835,1]
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
0.7158642462509865
kappa
0.8105387803433984
kappa
0.7410492243318991
kappa
0.7284817869687044
kappa
0.7789270064348032
kappa
0.7600252604988949
kappa
0.7789182787208844
kappa
0.7599778927006434
kappa
0.7789444597955236
kappa
0.753739295157662
kb_relative_information_score
93.8271302999774
kb_relative_information_score
93.55152272005634
kb_relative_information_score
93.8054211198396
kb_relative_information_score
93.74917099827104
kb_relative_information_score
93.44849176078085
kb_relative_information_score
94.09245818544994
kb_relative_information_score
96.7040735912743
kb_relative_information_score
94.3832491857109
kb_relative_information_score
95.2890386818828
kb_relative_information_score
95.20770968363784
mean_absolute_error
0.016423193599650613
mean_absolute_error
0.01658796451477926
mean_absolute_error
0.01660866762696649
mean_absolute_error
0.016590712692472278
mean_absolute_error
0.01662665026976807
mean_absolute_error
0.016484338651890744
mean_absolute_error
0.016245052649957355
mean_absolute_error
0.016503408321968113
mean_absolute_error
0.016468764101950768
mean_absolute_error
0.016397172742230365
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
0.71875
predictive_accuracy
0.8125
predictive_accuracy
0.74375
predictive_accuracy
0.73125
predictive_accuracy
0.78125
predictive_accuracy
0.7625
predictive_accuracy
0.78125
predictive_accuracy
0.7625
predictive_accuracy
0.78125
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
0.71875 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0,1,0,1,0,0,0,0,1,1,1,1,0,0,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,0.5,0,1,1,0,0.5,1,0,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,1,0.5,0,1,0,1]
recall
0.8125 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0.5,0.5,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0]
recall
0.74375 [0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,0,1,1,0,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,1,1,1,1,1,0,0,0,1,1,1,1,1,1,0,1]
recall
0.73125 [0.5,1,1,1,0.5,1,1,1,0,0.5,1,0.5,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,0.5,0,0,0,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,0,0.5]
recall
0.78125 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0,1,1,1,0,0,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,0.5,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1]
recall
0.7625 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,1,1,0,1,0,0.5,0.5,1,1,1,1,0,0,1,1,1,1,1,0.5,1,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,0,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0,0.5]
recall
0.78125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,0.5,1,1,0,0,1,1,1,0.5,0,1,1,1,1,0,0,1,1,1,0.5,1,1,0,0.5,0,0.5,1,1,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.7625 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,0,0.5,0,0.5,1,0.5,1,0.5,1,1,1,0,1,1,1,1,1,1,1,0,0,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,0.5,0,0.5,0.5,1,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5]
recall
0.78125 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0.5,0,0,1,0.5,1,1,1,1,0.5,1,1,0,1,0.5,0.5,0,0,1,0,1,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1]
recall
0.75625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,0,0.5,0.5,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0,1]
relative_absolute_error
0.829454222204575
relative_absolute_error
0.8377759855949108
relative_absolute_error
0.8388215973215385
relative_absolute_error
0.8379147824480934
relative_absolute_error
0.8397298116044465
relative_absolute_error
0.8325423561560968
relative_absolute_error
0.8204572045432994
relative_absolute_error
0.8335054708064689
relative_absolute_error
0.8317557627247849
relative_absolute_error
0.8281400374863808
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
0.08446739887829732
root_mean_squared_error
0.08489584120459122
root_mean_squared_error
0.08509269214449766
root_mean_squared_error
0.08499801614591526
root_mean_squared_error
0.08513188324206467
root_mean_squared_error
0.08466705000132628
root_mean_squared_error
0.08356309193797369
root_mean_squared_error
0.08456844704426146
root_mean_squared_error
0.08444180167062938
root_mean_squared_error
0.08434340024504873
root_relative_squared_error
0.8489293002927231
root_relative_squared_error
0.853235307688553
root_relative_squared_error
0.8552137340743059
root_relative_squared_error
0.8542622044865786
root_relative_squared_error
0.8556076194250696
root_relative_squared_error
0.8509358695777537
root_relative_squared_error
0.839840673576457
root_relative_squared_error
0.8499448725722886
root_relative_squared_error
0.8486720386760099
root_relative_squared_error
0.8476830671381702
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
1896.71004200045
usercpu_time_millis
1699.5795729999372
usercpu_time_millis
1685.8059849996607
usercpu_time_millis
1692.6598989998638
usercpu_time_millis
1699.0022440004395
usercpu_time_millis
1690.763020000304
usercpu_time_millis
1692.9229709994615
usercpu_time_millis
1696.1980820001372
usercpu_time_millis
1702.1498749995772
usercpu_time_millis
1692.5145309996878
usercpu_time_millis_testing
163.15538600019863
usercpu_time_millis_testing
172.75641100013672
usercpu_time_millis_testing
157.3207569999795
usercpu_time_millis_testing
161.8441939999684
usercpu_time_millis_testing
164.48104000028252
usercpu_time_millis_testing
155.68840500009173
usercpu_time_millis_testing
161.9215279997661
usercpu_time_millis_testing
161.41941600017162
usercpu_time_millis_testing
170.3565439997874
usercpu_time_millis_testing
161.39881899971442
usercpu_time_millis_training
1733.5546560002513
usercpu_time_millis_training
1526.8231619998005
usercpu_time_millis_training
1528.4852279996812
usercpu_time_millis_training
1530.8157049998954
usercpu_time_millis_training
1534.521204000157
usercpu_time_millis_training
1535.0746150002124
usercpu_time_millis_training
1531.0014429996954
usercpu_time_millis_training
1534.7786659999656
usercpu_time_millis_training
1531.7933309997898
usercpu_time_millis_training
1531.1157119999734