8701979
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)
6679398
axis
0
7660
categorical_features
[]
7660
copy
true
7660
fill_empty
0
7660
missing_values
"NaN"
7660
strategy
"median"
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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
4527.034977458508
7708
cache_size
200
7708
class_weight
null
7708
coef0
-0.9935953968704252
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decision_function_shape
null
7708
degree
1
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gamma
3.71214768222419e-05
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kernel
"sigmoid"
7708
max_iter
-1
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probability
true
7708
random_state
21183
7708
shrinking
true
7708
tol
1.861533522583847e-05
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
18320859
description
https://api.openml.org/data/download/18320859/description.xml
-1
18320860
predictions
https://api.openml.org/data/download/18320860/predictions.arff
area_under_roc_curve
0.9966414141414144 [0.98765,0.999961,1,1,0.998146,0.995147,0.999842,0.999882,0.99779,0.999961,0.998935,0.999684,0.999842,0.980942,0.999724,0.999566,1,0.990767,0.986861,0.970604,0.99858,0.999487,0.996252,0.999803,0.998737,0.980035,0.98765,0.999487,0.998777,1,0.999842,1,1,0.991517,0.999684,0.999408,0.994831,0.985993,0.999882,0.999921,0.999092,0.999408,1,0.999053,0.997751,0.999921,0.999329,0.999014,0.997751,0.99637,0.995186,0.999053,0.998264,0.991319,0.996962,0.989899,1,0.999566,0.993884,0.995581,0.999014,0.99929,0.997554,0.999132,0.999369,0.995265,0.986703,1,0.998501,0.9942,0.996607,0.999527,0.995936,0.998619,0.988281,0.999448,0.999566,0.990372,1,1,0.995028,0.997869,0.998422,0.997277,0.999369,0.999842,0.999961,0.999566,0.999408,1,0.99854,0.999605,0.998777,0.998067,0.997514,0.998224,0.998185,0.998185,0.972064,0.998264]
average_cost
0
f_measure
0.8289583523308767 [0.702703,0.9375,1,1,0.933333,0.827586,1,0.967742,0.875,0.933333,0.875,0.875,0.967742,0.625,0.967742,0.933333,1,0.4,0.451613,0.285714,0.83871,0.967742,0.769231,1,0.875,0.457143,0.5,0.848485,0.903226,0.933333,0.967742,1,0.969697,0.709677,0.875,0.875,0.628571,0.411765,0.914286,0.967742,0.777778,0.909091,1,0.875,0.9375,0.941176,0.857143,0.875,0.764706,0.8125,0.787879,0.827586,0.866667,0.685714,0.733333,0.625,1,0.9375,0.615385,0.727273,0.896552,0.903226,0.727273,0.9375,0.875,0.727273,0.5,1,0.83871,0.727273,0.774194,0.857143,0.666667,0.967742,0.736842,0.909091,0.9375,0.588235,1,1,0.75,0.866667,0.896552,0.533333,0.875,0.933333,0.933333,0.903226,0.967742,1,0.758621,0.740741,0.909091,0.903226,0.967742,0.823529,0.8,0.8,0.533333,0.903226]
kappa
0.8244949494949495
kb_relative_information_score
991.9560905075335
mean_absolute_error
0.01604719109615556
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
0.8378592708722603 [0.619048,0.9375,1,1,1,0.923077,1,1,0.875,1,0.875,0.875,1,0.625,1,1,1,0.428571,0.466667,0.263158,0.866667,1,0.652174,1,0.875,0.421053,0.5,0.823529,0.933333,1,1,1,0.941176,0.733333,0.875,0.875,0.578947,0.388889,0.842105,1,0.7,0.882353,1,0.875,0.9375,0.888889,1,0.875,0.722222,0.8125,0.764706,0.923077,0.928571,0.631579,0.785714,0.625,1,0.9375,0.521739,0.705882,1,0.933333,0.705882,0.9375,0.875,0.705882,0.5,1,0.866667,0.705882,0.8,1,0.647059,1,0.636364,0.882353,0.9375,0.555556,1,1,0.75,0.928571,1,0.571429,0.875,1,1,0.933333,1,1,0.846154,0.909091,0.882353,0.933333,1,0.777778,0.736842,0.736842,0.571429,0.933333]
predictive_accuracy
0.82625
prior_entropy
6.6438561897747395
recall
0.82625 [0.8125,0.9375,1,1,0.875,0.75,1,0.9375,0.875,0.875,0.875,0.875,0.9375,0.625,0.9375,0.875,1,0.375,0.4375,0.3125,0.8125,0.9375,0.9375,1,0.875,0.5,0.5,0.875,0.875,0.875,0.9375,1,1,0.6875,0.875,0.875,0.6875,0.4375,1,0.9375,0.875,0.9375,1,0.875,0.9375,1,0.75,0.875,0.8125,0.8125,0.8125,0.75,0.8125,0.75,0.6875,0.625,1,0.9375,0.75,0.75,0.8125,0.875,0.75,0.9375,0.875,0.75,0.5,1,0.8125,0.75,0.75,0.75,0.6875,0.9375,0.875,0.9375,0.9375,0.625,1,1,0.75,0.8125,0.8125,0.5,0.875,0.875,0.875,0.875,0.9375,1,0.6875,0.625,0.9375,0.875,0.9375,0.875,0.875,0.875,0.5,0.875]
relative_absolute_error
0.8104641967755319
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08236385250624866
root_relative_squared_error
0.8277878637921205
total_cost
0
area_under_roc_curve
0.9962075073640634 [0.981132,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.981132,1,1,0.981013,0.974843,1,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.977848,1,1,1,1,0.987421,1,1,1,1,0.996835,1,1,1,1,1,1,0.974684,1,1,0.962264,0.996835,1,1,1,1,1,1,0.962025,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,0.987342,1,0.993671,0.924051,1]
area_under_roc_curve
0.9964819182389937 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.898734,1,1,1,0.971519,0.993671,0.993711,1,1,1,1,1,0.984177,0.971519,1,1,1,1,1,1,1,1,1,0.993711,0.993711,1,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,1,1,1,1,0.993711,1,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.987342,1,1,0.981013,0.996835,1,0.987421,0.996835,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.996835,1,0.974843]
area_under_roc_curve
0.9969182887508957 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.990506,1,0.933544,1,1,0.993671,1,1,0.96519,0.993671,1,1,1,1,1,1,0.984177,1,1,0.993711,1,1,1,0.996835,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.984177,1,0.993711,1,0.987421,1,0.993671,1,1,1,0.984177,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.968553,1]
area_under_roc_curve
0.9972352420189475 [1,1,1,1,0.996835,0.996835,1,1,0.996835,1,1,1,1,1,1,1,1,0.993671,0.949686,0.955696,1,1,0.996835,1,0.993711,0.993671,1,1,1,1,1,1,1,0.990506,1,1,1,0.981132,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.984177,0.981013,1,0.981132,1,0.993711,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.996835,1,1,1]
area_under_roc_curve
0.9968391748268448 [1,1,1,1,1,0.993671,1,1,1,1,0.993671,1,1,0.968553,1,1,1,0.996835,0.987421,0.990506,1,1,1,1,0.996835,0.996835,0.987342,1,1,1,1,1,1,0.981013,1,1,1,0.977848,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.993671,0.996835,1,0.996835,0.996835,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.939873,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.974684,0.993711,1,1,0.981132,1]
area_under_roc_curve
0.9960863486187405 [0.914557,1,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.968354,1,1,0.996835,1,0.993671,0.987342,0.974684,1,1,1,1,1,1,0.996835,1,0.996835,0.996835,0.984177,1,1,0.996835,1,1,1,1,1,1,1,0.990506,0.996835,1,1,1,1,0.996835,0.990506,1,1,0.996835,1,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,0.996835,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.949686,1]
area_under_roc_curve
0.996367725499562 [0.996835,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.993671,0.981013,1,1,0.984177,1,1,0.90566,0.962264,1,1,1,0.996835,1,1,1,1,1,0.977848,0.993671,1,1,1,1,1,1,0.981132,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,1,0.996835,1,1,1,1,0.996835,1,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.968354,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.93038,1]
area_under_roc_curve
0.9974380025475679 [0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.993671,0.996835,1,1,1,1,0.996835,0.987421,1,1,0.974843,1,1,1,1,1,1,1,0.990506,0.993671,1,1,0.996835,1,1,1,1,1,1,0.993671,1,0.981132,0.949686,1,0.996835,1,1,0.984177,1,1,0.996835,1,1,1,1,1,1,0.974843,0.937107,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835]
area_under_roc_curve
0.9978285964493272 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,0.968553,1,1,0.987421,1,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,0.962025,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.990506,1,1,0.96519,0.996835,0.987421,1,1,1,1,1,1,1,1,1,1,0.990506,1,0.996835,1,1,0.993711,0.981013,1,1,1,1,0.993711,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.9977492337393519 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.993711,0.971519,0.987421,0.996835,1,1,1,1,0.993711,0.993711,0.996835,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.984177,1,1,1,0.993671,1,0.993711,0.993711,0.981013,1,1,1,1,1,0.993671,0.990506,1,0.993671,1,1,1,1,1,0.968553,1,0.993711,1,1,1,0.996835,1,1,0.993671,1,1,0.993671,1,1,0.993711,1,1,1]
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
kappa
0.8042002210642666
kappa
0.8357289527720739
kappa
0.8673195387774444
kappa
0.8041924914136829
kappa
0.816781836130306
kappa
0.7978681405448086
kappa
0.8230927183699257
kappa
0.8230717586193278
kappa
0.8610014215763703
kappa
0.8105462582886012
kb_relative_information_score
99.08627887593155
kb_relative_information_score
98.11772360862233
kb_relative_information_score
99.29579223228626
kb_relative_information_score
98.48123876168873
kb_relative_information_score
98.88331058928739
kb_relative_information_score
98.09360397515007
kb_relative_information_score
101.28338355795755
kb_relative_information_score
98.73202955919713
kb_relative_information_score
100.5443979925504
kb_relative_information_score
99.43833135486415
mean_absolute_error
0.015974382699601754
mean_absolute_error
0.01619140218661016
mean_absolute_error
0.016070778491462163
mean_absolute_error
0.016171482302059642
mean_absolute_error
0.016162402331315863
mean_absolute_error
0.016135095062707955
mean_absolute_error
0.015786191300774514
mean_absolute_error
0.01610473852137336
mean_absolute_error
0.015889334762181097
mean_absolute_error
0.015986103303469314
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
0.80625
predictive_accuracy
0.8375
predictive_accuracy
0.86875
predictive_accuracy
0.80625
predictive_accuracy
0.81875
predictive_accuracy
0.8
predictive_accuracy
0.825
predictive_accuracy
0.825
predictive_accuracy
0.8625
predictive_accuracy
0.8125
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
0.80625 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,0,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.8375 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0,0.5,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0]
recall
0.86875 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0,1]
recall
0.80625 [0.5,1,1,1,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,0.5,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1]
recall
0.81875 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,0,0,0.5,1,0.5,0.5,1,1,0.5,1,1,1,0,1,0.5,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1]
recall
0.8 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,1]
recall
0.825 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.825 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5]
recall
0.8625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,0,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1]
recall
0.8125 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.8067870050303902
relative_absolute_error
0.8177475851823298
relative_absolute_error
0.8116554793667745
relative_absolute_error
0.8167415304070513
relative_absolute_error
0.8162829460260523
relative_absolute_error
0.814903791045855
relative_absolute_error
0.7972823889280045
relative_absolute_error
0.8133706323925927
relative_absolute_error
0.8024916546556097
relative_absolute_error
0.8073789547206711
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
0.08215710693843328
root_mean_squared_error
0.08292328151765775
root_mean_squared_error
0.0823646813702066
root_mean_squared_error
0.08288296843022996
root_mean_squared_error
0.08276106080527604
root_mean_squared_error
0.08294545181952424
root_mean_squared_error
0.08114186605538588
root_mean_squared_error
0.08252512425530006
root_mean_squared_error
0.08162997086828284
root_mean_squared_error
0.08228767889617207
root_relative_squared_error
0.825709992654204
root_relative_squared_error
0.8334103369063129
root_relative_squared_error
0.8277961941883352
root_relative_squared_error
0.8330051751331652
root_relative_squared_error
0.831779957402694
root_relative_squared_error
0.8336331568238283
root_relative_squared_error
0.815506437863606
root_relative_squared_error
0.8294087058554175
root_relative_squared_error
0.8204120758113206
root_relative_squared_error
0.8270222902056031
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
1885.99585799966
usercpu_time_millis
1682.90423200051
usercpu_time_millis
1695.6769790003818
usercpu_time_millis
1689.9952919993666
usercpu_time_millis
2156.827004999286
usercpu_time_millis
1726.3743560006333
usercpu_time_millis
1725.8494229999997
usercpu_time_millis
1709.2396539992478
usercpu_time_millis
1709.0661920001367
usercpu_time_millis
1683.5884259990053
usercpu_time_millis_testing
149.61012799994933
usercpu_time_millis_testing
155.4464150003696
usercpu_time_millis_testing
148.77296800023032
usercpu_time_millis_testing
149.18774599937024
usercpu_time_millis_testing
191.02034299976367
usercpu_time_millis_testing
148.14538700011326
usercpu_time_millis_testing
157.32700700027635
usercpu_time_millis_testing
148.55560099931608
usercpu_time_millis_testing
156.42353000021103
usercpu_time_millis_testing
148.77930299917352
usercpu_time_millis_training
1736.3857299997107
usercpu_time_millis_training
1527.4578170001405
usercpu_time_millis_training
1546.9040110001515
usercpu_time_millis_training
1540.8075459999964
usercpu_time_millis_training
1965.8066619995225
usercpu_time_millis_training
1578.22896900052
usercpu_time_millis_training
1568.5224159997233
usercpu_time_millis_training
1560.6840529999317
usercpu_time_millis_training
1552.6426619999256
usercpu_time_millis_training
1534.8091229998317