8803059
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)
6778140
axis
0
7660
categorical_features
[]
7660
copy
true
7660
fill_empty
0
7660
missing_values
"NaN"
7660
strategy
"most_frequent"
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
14839.356701618754
7708
cache_size
200
7708
class_weight
null
7708
coef0
-0.31772049739763175
7708
decision_function_shape
null
7708
degree
4
7708
gamma
1.23794777956786
7708
kernel
"poly"
7708
max_iter
-1
7708
probability
true
7708
random_state
50059
7708
shrinking
true
7708
tol
0.0068565365371139496
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
18522123
description
https://api.openml.org/data/download/18522123/description.xml
-1
18522124
predictions
https://api.openml.org/data/download/18522124/predictions.arff
area_under_roc_curve
0.9959071180555553 [0.985756,0.999448,0.999961,0.999487,0.998303,0.993213,0.999724,0.998777,0.996528,0.999882,0.998658,0.999605,0.999211,0.960227,0.998698,0.999171,1,0.991951,0.983941,0.974471,0.997396,0.999605,0.994555,0.999092,0.998777,0.978614,0.98986,0.998737,0.998185,1,0.99925,0.999921,1,0.990412,0.997475,0.998422,0.993213,0.983389,0.99925,0.999645,0.998658,0.997317,1,0.998698,0.995896,1,0.999014,0.998737,0.997356,0.996804,0.995028,0.998737,0.997988,0.993174,0.995818,0.992819,1,0.999171,0.993134,0.996528,0.997396,0.999566,0.996962,0.999053,0.999014,0.993056,0.985874,0.999724,0.997672,0.992858,0.996962,0.99858,0.995107,0.997711,0.989583,0.999369,0.99929,0.990688,0.999566,0.999961,0.996686,0.996567,0.997751,0.995423,0.99925,0.999961,0.999842,0.999053,0.999092,0.999803,0.999487,0.999605,0.99779,0.996252,0.994673,0.996567,0.996922,0.998106,0.966422,0.995778]
average_cost
0
f_measure
0.805684472591728 [0.648649,0.903226,0.967742,0.857143,0.857143,0.8,0.9375,0.756757,0.83871,0.896552,0.848485,0.866667,0.882353,0.625,0.896552,0.875,0.933333,0.580645,0.411765,0.242424,0.774194,0.9375,0.764706,0.882353,0.882353,0.411765,0.588235,0.848485,0.903226,1,0.9375,0.967742,0.9375,0.580645,0.83871,0.875,0.5625,0.533333,0.9375,0.896552,0.705882,0.875,0.933333,0.848485,0.827586,0.888889,0.896552,0.8125,0.685714,0.8125,0.848485,0.758621,0.787879,0.727273,0.774194,0.606061,0.933333,0.969697,0.631579,0.722222,0.827586,0.882353,0.689655,0.896552,0.909091,0.722222,0.451613,0.9375,0.83871,0.6875,0.787879,0.827586,0.727273,0.933333,0.7,0.9375,0.896552,0.631579,1,1,0.848485,0.8125,0.83871,0.545455,0.875,0.866667,0.967742,0.8,0.903226,0.933333,0.8125,0.827586,0.866667,0.8,0.909091,0.823529,0.810811,0.83871,0.606061,0.866667]
kappa
0.8005050505050505
kb_relative_information_score
970.4495532565624
mean_absolute_error
0.01591189338788163
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
0.8164975317510144 [0.571429,0.933333,1,1,1,0.857143,0.9375,0.666667,0.866667,1,0.823529,0.928571,0.833333,0.625,1,0.875,1,0.6,0.388889,0.235294,0.8,0.9375,0.722222,0.833333,0.833333,0.388889,0.555556,0.823529,0.933333,1,0.9375,1,0.9375,0.6,0.866667,0.875,0.5625,0.571429,0.9375,1,0.666667,0.875,1,0.823529,0.923077,0.8,1,0.8125,0.631579,0.8125,0.823529,0.846154,0.764706,0.705882,0.8,0.588235,1,0.941176,0.545455,0.65,0.923077,0.833333,0.769231,1,0.882353,0.65,0.466667,0.9375,0.866667,0.6875,0.764706,0.923077,0.705882,1,0.583333,0.9375,1,0.545455,1,1,0.823529,0.8125,0.866667,0.529412,0.875,0.928571,1,0.857143,0.933333,1,0.8125,0.923077,0.928571,0.857143,0.882353,0.777778,0.714286,0.866667,0.588235,0.928571]
predictive_accuracy
0.8025
prior_entropy
6.6438561897747395
recall
0.8025 [0.75,0.875,0.9375,0.75,0.75,0.75,0.9375,0.875,0.8125,0.8125,0.875,0.8125,0.9375,0.625,0.8125,0.875,0.875,0.5625,0.4375,0.25,0.75,0.9375,0.8125,0.9375,0.9375,0.4375,0.625,0.875,0.875,1,0.9375,0.9375,0.9375,0.5625,0.8125,0.875,0.5625,0.5,0.9375,0.8125,0.75,0.875,0.875,0.875,0.75,1,0.8125,0.8125,0.75,0.8125,0.875,0.6875,0.8125,0.75,0.75,0.625,0.875,1,0.75,0.8125,0.75,0.9375,0.625,0.8125,0.9375,0.8125,0.4375,0.9375,0.8125,0.6875,0.8125,0.75,0.75,0.875,0.875,0.9375,0.8125,0.75,1,1,0.875,0.8125,0.8125,0.5625,0.875,0.8125,0.9375,0.75,0.875,0.875,0.8125,0.75,0.8125,0.75,0.9375,0.875,0.9375,0.8125,0.625,0.8125]
relative_absolute_error
0.8036309791859395
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08237054817894275
root_relative_squared_error
0.8278551578346822
total_cost
0
area_under_roc_curve
0.9964453467080647 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.984177,1,0.981132,1,0.996835,0.981013,0.987421,0.987421,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.990506,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.977848,1,1,0.949686,0.996835,1,1,1,1,1,1,0.952532,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.987342,0.993711,0.993671,0.946203,1]
area_under_roc_curve
0.99557310325611 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.879747,1,1,1,0.984177,0.993671,0.987421,1,0.993711,0.993711,1,1,0.984177,0.968354,1,1,1,1,1,1,0.996835,0.990506,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.987421,1,1,1,1,1,1,1,0.996835,0.993671,1,1,0.974684,0.990506,1,0.981132,0.993671,1,1,1,1,1,1,1,1,0.962025,1,1,1,0.996835,1,0.981132]
area_under_roc_curve
0.9964831621686171 [1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,0.996835,1,1,0.993671,1,0.939873,1,1,0.993671,1,1,0.943038,0.990506,0.993711,1,1,1,1,1,0.984177,1,1,0.993711,1,0.993711,0.996835,0.996835,0.974843,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.984177,1,1,1,0.987421,1,0.990506,1,1,0.993711,0.987342,1,0.996835,1,1,1,1,1,0.996835,0.993671,1,1,1,1,0.996835,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,1]
area_under_roc_curve
0.9969991441764188 [1,1,1,0.993711,1,0.987342,1,1,0.996835,1,1,1,1,1,1,1,1,0.987342,0.943396,0.981013,1,1,0.996835,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,0.977848,0.987342,0.996835,0.987421,1,0.993711,1,1,0.993671,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.990506,1,1,1]
area_under_roc_curve
0.9958947834567311 [1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.899371,1,1,1,0.990506,0.987421,0.987342,1,1,1,1,1,0.990506,0.987342,1,1,1,1,1,1,0.984177,1,1,0.996835,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.993671,0.987342,1,0.993671,1,1,0.996835,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.952532,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.96519,0.993711,1,1,0.924528,1]
area_under_roc_curve
0.9960057419791417 [0.911392,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.968354,1,1,1,1,0.993671,0.984177,0.981013,1,1,1,1,1,1,0.993671,1,0.993671,1,0.987342,0.996835,1,0.990506,1,1,0.993711,1,1,1,1,0.993671,0.993671,1,1,0.993671,1,0.996835,0.987342,1,1,0.996835,1,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,0.996835,1,1,0.996835,1,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.993671]
area_under_roc_curve
0.9950237839343998 [0.996835,1,1,1,1,1,1,0.996835,0.993711,1,1,1,0.996835,1,1,1,1,1,1,0.971519,0.996835,1,0.990506,1,1,0.899371,0.962264,0.993671,1,1,0.996835,1,1,1,1,1,0.974684,0.993671,1,1,1,1,1,1,0.968553,1,0.996835,0.996835,1,1,1,1,1,1,0.996835,1,1,0.996835,0.993671,1,1,1,1,1,1,0.981132,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,0.838608,1]
area_under_roc_curve
0.9970421841413899 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.993711,0.993671,0.981013,0.996835,1,0.996835,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.987421,0.962264,1,1,1,1,0.987342,1,1,0.993671,1,1,1,1,1,1,0.981132,0.918239,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.996835,1,0.990506,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.993671]
area_under_roc_curve
0.9969979002467955 [1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,0.993671,0.981132,0.996835,1,0.974843,0.990506,1,0.987421,1,1,1,1,1,1,1,0.993711,1,1,1,0.955696,1,1,1,1,1,0.996835,1,1,1,0.993671,0.993711,1,0.993671,1,1,0.981013,0.974684,1,1,1,1,1,0.993671,1,1,1,1,1,0.984177,1,1,1,1,0.993711,0.981013,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,0.996835,1,1,0.990506,1,1,1,1]
area_under_roc_curve
0.9973534153331742 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,1,0.962025,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.987342,0.996835,0.993711,0.987421,0.984177,1,1,1,1,1,0.990506,0.990506,1,0.990506,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.993671,1,1,1,1,1,1,1,1,0.993711,1,1,1]
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
0.7726285872182529
kappa
0.7725657427149965
kappa
0.83575489576753
kappa
0.7852179406190777
kappa
0.829397361977727
kappa
0.8041615667074664
kappa
0.8104639684106614
kappa
0.8104415133085854
kappa
0.8041847611527833
kappa
0.7789793582507795
kb_relative_information_score
98.40216605959982
kb_relative_information_score
95.38487012153213
kb_relative_information_score
96.06365905425113
kb_relative_information_score
95.10522974337584
kb_relative_information_score
96.56111089716015
kb_relative_information_score
96.63757157491347
kb_relative_information_score
101.08104852698465
kb_relative_information_score
97.12729678455221
kb_relative_information_score
96.97908214638923
kb_relative_information_score
97.10751834780227
mean_absolute_error
0.015693219880814895
mean_absolute_error
0.016161814339707405
mean_absolute_error
0.016110083281104193
mean_absolute_error
0.01622350229517602
mean_absolute_error
0.016087336374100664
mean_absolute_error
0.015911019095940268
mean_absolute_error
0.015321748055983136
mean_absolute_error
0.015964366349476573
mean_absolute_error
0.01586795053186965
mean_absolute_error
0.015777893674643485
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
0.775
predictive_accuracy
0.775
predictive_accuracy
0.8375
predictive_accuracy
0.7875
predictive_accuracy
0.83125
predictive_accuracy
0.80625
predictive_accuracy
0.8125
predictive_accuracy
0.8125
predictive_accuracy
0.80625
predictive_accuracy
0.78125
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
0.775 [1,0.5,1,0,0.5,1,1,1,1,1,0.5,0.5,1,0,1,0,0.5,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.775 [1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,0.5,1,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,0,1,1,1,1,0.5,0]
recall
0.8375 [1,1,1,1,0.5,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1]
recall
0.7875 [0,1,1,0,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,0.5,0,0,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0,1,1,1,1,0.5,1,1]
recall
0.83125 [1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1]
recall
0.80625 [0.5,1,0.5,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5]
recall
0.8125 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.8125 [0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5]
recall
0.80625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,0,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0,0,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1]
recall
0.78125 [1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.7925868626674176
relative_absolute_error
0.8162532494801706
relative_absolute_error
0.8136405697527357
relative_absolute_error
0.8193688027866664
relative_absolute_error
0.8124917360656887
relative_absolute_error
0.803586823027285
relative_absolute_error
0.7738256593930863
relative_absolute_error
0.8062811287614418
relative_absolute_error
0.8014116430237184
relative_absolute_error
0.7968633169011848
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
0.08150715520806978
root_mean_squared_error
0.08325607986441917
root_mean_squared_error
0.08300714501079098
root_mean_squared_error
0.08364848742757307
root_mean_squared_error
0.0828705122547894
root_mean_squared_error
0.08247887194624014
root_mean_squared_error
0.07999231716859494
root_mean_squared_error
0.08229651776833329
root_mean_squared_error
0.08235043669840798
root_mean_squared_error
0.0822402403365912
root_relative_squared_error
0.819177731983119
root_relative_squared_error
0.8367550861398226
root_relative_squared_error
0.8342531967254994
root_relative_squared_error
0.8406989305394564
root_relative_squared_error
0.8328799858596525
root_relative_squared_error
0.8289438526589344
root_relative_squared_error
0.8039530368464657
root_relative_squared_error
0.8271111242132656
root_relative_squared_error
0.8276530298500995
root_relative_squared_error
0.826545514742801
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
1580.9650230000045
usercpu_time_millis
1369.9782690000006
usercpu_time_millis
1366.9189810000048
usercpu_time_millis
1367.860892000003
usercpu_time_millis
1388.1270689999994
usercpu_time_millis
1376.0433670000011
usercpu_time_millis
1381.0159070000054
usercpu_time_millis
1454.0984300000018
usercpu_time_millis
1381.9803880000022
usercpu_time_millis
1381.7794340000019
usercpu_time_millis_testing
134.03645700000055
usercpu_time_millis_testing
132.78192399999966
usercpu_time_millis_testing
132.64321500000165
usercpu_time_millis_testing
132.33547699999804
usercpu_time_millis_testing
140.79004399999917
usercpu_time_millis_testing
133.5063360000035
usercpu_time_millis_testing
134.12684300000421
usercpu_time_millis_testing
132.94725600000135
usercpu_time_millis_testing
132.80822000000114
usercpu_time_millis_testing
134.47893900000452
usercpu_time_millis_training
1446.928566000004
usercpu_time_millis_training
1237.196345000001
usercpu_time_millis_training
1234.2757660000032
usercpu_time_millis_training
1235.525415000005
usercpu_time_millis_training
1247.3370250000003
usercpu_time_millis_training
1242.5370309999976
usercpu_time_millis_training
1246.8890640000013
usercpu_time_millis_training
1321.1511740000005
usercpu_time_millis_training
1249.1721680000012
usercpu_time_millis_training
1247.3004949999975