8814279
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)
6789329
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
20481.038842503924
7708
cache_size
200
7708
class_weight
null
7708
coef0
-0.3408960988676617
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decision_function_shape
null
7708
degree
5
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gamma
0.01822101335315657
7708
kernel
"rbf"
7708
max_iter
-1
7708
probability
true
7708
random_state
18241
7708
shrinking
false
7708
tol
2.62294297866565e-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
18544478
description
https://api.openml.org/data/download/18544478/description.xml
-1
18544479
predictions
https://api.openml.org/data/download/18544479/predictions.arff
area_under_roc_curve
0.996863557449495 [0.991872,0.999882,1,0.999014,0.998027,0.995107,1,0.999921,0.999092,0.999724,0.999132,0.999487,0.999842,0.96658,1,0.99925,1,0.991004,0.987255,0.975694,0.998343,1,0.997711,1,0.999527,0.979877,0.99199,0.999408,0.999092,0.999961,0.999882,0.999605,1,0.990333,0.999448,0.99929,0.994555,0.98402,0.999684,0.999921,0.998895,0.999487,1,0.999092,0.999605,0.999961,0.998698,0.999369,0.998185,0.998698,0.996528,0.998856,0.998658,0.993056,0.99783,0.994279,1,0.999882,0.995068,0.997238,0.999645,0.999605,0.996567,0.999605,0.999763,0.995541,0.987571,1,0.998698,0.995699,0.996883,0.999605,0.996528,0.99929,0.983428,0.999842,0.999921,0.989938,1,0.999803,0.995818,0.998224,0.998461,0.996686,0.999566,0.999921,1,0.99925,0.999487,1,0.999684,0.999566,0.998935,0.999171,0.99854,0.999211,0.998658,0.998382,0.972814,0.999132]
average_cost
0
f_measure
0.8461614030193089 [0.742857,0.967742,1,0.967742,0.933333,0.848485,1,1,0.866667,0.933333,0.909091,0.903226,0.9375,0.8125,0.914286,0.933333,1,0.6,0.4,0.193548,0.727273,0.967742,0.857143,1,0.9375,0.424242,0.545455,0.848485,0.933333,0.967742,0.967742,0.967742,1,0.580645,0.875,0.875,0.516129,0.625,0.9375,1,0.8,0.909091,1,0.848485,0.9375,0.9375,0.9375,0.909091,0.8,0.8125,0.8125,0.866667,0.903226,0.758621,0.733333,0.580645,1,0.9375,0.666667,0.727273,0.896552,0.933333,0.774194,0.875,0.909091,0.702703,0.551724,1,0.903226,0.742857,0.787879,0.9375,0.764706,0.967742,0.8,0.9375,0.9375,0.685714,0.967742,0.969697,0.848485,0.848485,0.903226,0.571429,0.875,0.967742,0.967742,0.875,0.903226,0.967742,0.83871,0.903226,0.903226,0.9375,0.967742,0.903226,0.823529,0.787879,0.545455,0.9375]
kappa
0.8434343434343434
kb_relative_information_score
953.3023229363815
mean_absolute_error
0.016574375527005854
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
0.8509766001542318 [0.684211,1,1,1,1,0.823529,1,1,0.928571,1,0.882353,0.933333,0.9375,0.8125,0.842105,1,1,0.642857,0.368421,0.2,0.705882,1,0.789474,1,0.9375,0.411765,0.529412,0.823529,1,1,1,1,1,0.6,0.875,0.875,0.533333,0.625,0.9375,1,0.736842,0.882353,1,0.823529,0.9375,0.9375,0.9375,0.882353,0.736842,0.8125,0.8125,0.928571,0.933333,0.846154,0.785714,0.6,1,0.9375,0.6,0.705882,1,1,0.8,0.875,0.882353,0.619048,0.615385,1,0.933333,0.684211,0.764706,0.9375,0.722222,1,0.736842,0.9375,0.9375,0.631579,1,0.941176,0.823529,0.823529,0.933333,0.666667,0.875,1,1,0.875,0.933333,1,0.866667,0.933333,0.933333,0.9375,1,0.933333,0.777778,0.764706,0.529412,0.9375]
predictive_accuracy
0.845
prior_entropy
6.6438561897747395
recall
0.845 [0.8125,0.9375,1,0.9375,0.875,0.875,1,1,0.8125,0.875,0.9375,0.875,0.9375,0.8125,1,0.875,1,0.5625,0.4375,0.1875,0.75,0.9375,0.9375,1,0.9375,0.4375,0.5625,0.875,0.875,0.9375,0.9375,0.9375,1,0.5625,0.875,0.875,0.5,0.625,0.9375,1,0.875,0.9375,1,0.875,0.9375,0.9375,0.9375,0.9375,0.875,0.8125,0.8125,0.8125,0.875,0.6875,0.6875,0.5625,1,0.9375,0.75,0.75,0.8125,0.875,0.75,0.875,0.9375,0.8125,0.5,1,0.875,0.8125,0.8125,0.9375,0.8125,0.9375,0.875,0.9375,0.9375,0.75,0.9375,1,0.875,0.875,0.875,0.5,0.875,0.9375,0.9375,0.875,0.875,0.9375,0.8125,0.875,0.875,0.9375,0.9375,0.875,0.875,0.8125,0.5625,0.9375]
relative_absolute_error
0.8370896730811022
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08429589221791786
root_relative_squared_error
0.8472055935002212
total_cost
0
area_under_roc_curve
0.9969175423931216 [0.981132,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,0.993671,0.981013,0.993711,1,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.971519,1,1,0.968553,0.996835,1,1,1,1,1,1,0.949367,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.990506,1,0.993671,0.936709,1]
area_under_roc_curve
0.9962841334288672 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.851266,1,1,1,0.974684,0.993671,0.981132,1,1,1,1,1,0.987342,0.974684,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.987421,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.981132]
area_under_roc_curve
0.9971526450919512 [1,1,1,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,0.936709,1,1,0.993671,1,1,0.949367,1,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,0.987421,0.981013,1,1,1,0.993711,1,0.996835,1,1,1,0.990506,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1]
area_under_roc_curve
0.9977099255632516 [1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,1,1,1,1,1,0.990506,0.962264,0.977848,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.968553,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,1,1,1,1,1,1,1,0.996835,0.990506,1,1,0.987342,0.987342,1,0.987421,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.981132,1]
area_under_roc_curve
0.9968016081522169 [1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,0.91195,1,1,1,0.993671,0.987421,0.987342,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.981013,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.987421,1,1,0.984177,0.984177,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,1,1,1,1,0.943038,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,0.981013,1,1,1,0.987421,1]
area_under_roc_curve
0.9973897380781782 [0.949367,1,1,0.996835,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,0.971519,1,1,1,1,0.996835,0.996835,0.981013,1,1,1,1,1,1,0.993671,1,1,1,0.981013,1,1,0.996835,1,1,1,1,1,1,1,0.996835,0.996835,1,1,0.996835,1,1,0.996835,1,1,1,1,0.996835,1,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1]
area_under_roc_curve
0.9963667303558633 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.981013,0.993671,1,1,1,1,0.899371,0.962264,0.996835,1,1,0.996835,1,1,1,1,1,0.971519,0.993671,1,0.993711,1,0.996835,1,1,1,0.996835,1,0.993671,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.901899,1]
area_under_roc_curve
0.9979487600509511 [0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.987342,0.990506,0.993671,1,1,1,1,0.993711,1,1,0.993711,0.996835,1,1,1,1,1,1,0.987342,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,1,1,0.990506,1,1,0.993671,0.987421,1,1,1,1,1,0.981132,0.955975,1,1,0.993671,1,1,1,1,1,1,1,0.968553,1,1,0.996835,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.997789039487302 [1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.993671,0.955975,1,1,1,1,1,0.987421,0.993711,1,1,1,1,1,1,0.993711,1,1,1,0.949367,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.993671,1,1,0.974684,0.996835,0.993711,1,1,0.996835,1,1,1,1,1,1,1,0.987342,1,0.996835,1,1,0.993711,0.990506,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.9981045000398054 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.977848,1,1,1,1,0.977848,0.987421,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.993711,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.987342,1,1,1,0.984177,1,0.993711,0.993711,0.987342,1,0.996835,1,1,1,0.993671,1,1,1,1,1,1,1,1,0.968553,1,0.993711,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1]
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
0.8231346229767075
kappa
0.8483591991470205
kappa
0.8673404927353128
kappa
0.8610288601997711
kappa
0.842047069973148
kappa
0.848365187174222
kappa
0.8357354392892399
kappa
0.8294040990403981
kappa
0.8673352548663482
kappa
0.8105387803433984
kb_relative_information_score
95.15870122602348
kb_relative_information_score
94.76287244744604
kb_relative_information_score
95.58350032906873
kb_relative_information_score
95.1937791912942
kb_relative_information_score
94.83037893275365
kb_relative_information_score
94.4046321453603
kb_relative_information_score
97.02550829891511
kb_relative_information_score
95.5714471907994
kb_relative_information_score
96.04578371831414
kb_relative_information_score
94.72571945640522
mean_absolute_error
0.016579291203344507
mean_absolute_error
0.016617454798199757
mean_absolute_error
0.01655557039766469
mean_absolute_error
0.01664164607445823
mean_absolute_error
0.016563951398932915
mean_absolute_error
0.01665084103584489
mean_absolute_error
0.01638955066895524
mean_absolute_error
0.01657653538687985
mean_absolute_error
0.01651035986106454
mean_absolute_error
0.016658554444713935
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
0.825
predictive_accuracy
0.85
predictive_accuracy
0.86875
predictive_accuracy
0.8625
predictive_accuracy
0.84375
predictive_accuracy
0.85
predictive_accuracy
0.8375
predictive_accuracy
0.83125
predictive_accuracy
0.86875
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
0.825 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1]
recall
0.85 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0]
recall
0.86875 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1]
recall
0.8625 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1]
recall
0.84375 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,0,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,0,0,0.5,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1]
recall
0.85 [0.5,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
recall
0.8375 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1]
recall
0.83125 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
recall
0.86875 [1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1]
recall
0.8125 [1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.8373379395628525
relative_absolute_error
0.8392653938484712
relative_absolute_error
0.8361399190739729
relative_absolute_error
0.840487175477687
relative_absolute_error
0.8365632019663074
relative_absolute_error
0.8409515674669124
relative_absolute_error
0.8277550842906675
relative_absolute_error
0.8371987569131224
relative_absolute_error
0.8338565586396218
relative_absolute_error
0.8413411335714095
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
0.08433716222534791
root_mean_squared_error
0.08449293104882048
root_mean_squared_error
0.08415484972472892
root_mean_squared_error
0.08459066239714515
root_mean_squared_error
0.08424939849579732
root_mean_squared_error
0.08472449745364191
root_mean_squared_error
0.08340416306031244
root_mean_squared_error
0.08431401779255125
root_mean_squared_error
0.08396867396582659
root_mean_squared_error
0.08471419825716611
root_relative_squared_error
0.8476203726812536
root_relative_squared_error
0.8491859082615373
root_relative_squared_error
0.8457880631080882
root_relative_squared_error
0.850168145269563
root_relative_squared_error
0.8467383140111885
root_relative_squared_error
0.8515132381974289
root_relative_squared_error
0.8382433782565945
root_relative_squared_error
0.84738776237952
root_relative_squared_error
0.8439169263282738
root_relative_squared_error
0.8514097273781792
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
3128.989972999989
usercpu_time_millis
2949.378288999924
usercpu_time_millis
2925.6524370000534
usercpu_time_millis
3038.746557999957
usercpu_time_millis
2822.3780090002037
usercpu_time_millis
2896.139919999996
usercpu_time_millis
3077.3225160000948
usercpu_time_millis
3005.644826999969
usercpu_time_millis
2846.3847559996793
usercpu_time_millis
3122.6647020000655
usercpu_time_millis_testing
202.01508900004228
usercpu_time_millis_testing
207.25608599991574
usercpu_time_millis_testing
147.47216099999605
usercpu_time_millis_testing
163.94307399991703
usercpu_time_millis_testing
199.765626000044
usercpu_time_millis_testing
199.7279790000448
usercpu_time_millis_testing
184.93297300005906
usercpu_time_millis_testing
200.82710800011228
usercpu_time_millis_testing
161.4060059998792
usercpu_time_millis_testing
200.42024899998978
usercpu_time_millis_training
2926.9748839999465
usercpu_time_millis_training
2742.122203000008
usercpu_time_millis_training
2778.1802760000573
usercpu_time_millis_training
2874.80348400004
usercpu_time_millis_training
2622.6123830001598
usercpu_time_millis_training
2696.411940999951
usercpu_time_millis_training
2892.3895430000357
usercpu_time_millis_training
2804.817718999857
usercpu_time_millis_training
2684.9787499998
usercpu_time_millis_training
2922.2444530000757