8830050
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)
6805100
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
5040.179471452119
7708
cache_size
200
7708
class_weight
null
7708
coef0
0.3058714945438783
7708
decision_function_shape
null
7708
degree
4
7708
gamma
0.10836485505589782
7708
kernel
"rbf"
7708
max_iter
-1
7708
probability
true
7708
random_state
42611
7708
shrinking
true
7708
tol
1.1628969117765793e-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
18575886
description
https://api.openml.org/data/download/18575886/description.xml
-1
18575887
predictions
https://api.openml.org/data/download/18575887/predictions.arff
area_under_roc_curve
0.9944298453282825 [0.992424,0.999684,1,0.998224,0.997435,0.976997,0.999882,0.999921,0.997238,0.966501,0.997711,0.99929,0.99925,0.940972,0.99779,0.997001,1,0.985953,0.987492,0.971315,0.996765,0.999724,0.993529,1,0.997751,0.980784,0.988005,0.998619,0.999369,0.999803,0.999566,0.988834,0.999645,0.991083,0.997869,0.998856,0.993371,0.984493,0.999053,0.998619,0.996567,0.994003,1,0.998146,0.998856,0.998935,0.980548,0.999171,0.997869,0.99854,0.996094,0.99712,0.996054,0.994358,0.994989,0.991162,1,0.999921,0.995541,0.988321,0.998816,0.999408,0.993884,0.999803,0.999487,0.99349,0.986466,1,0.994989,0.996291,0.995936,0.998027,0.994397,0.995147,0.974392,0.999961,0.999408,0.973958,1,0.998856,0.990846,0.995344,0.995502,0.996488,0.999211,0.999763,0.999882,0.99783,0.998343,0.99929,0.999132,0.998974,0.997869,0.998816,0.999763,0.99858,0.994121,0.997554,0.967487,0.998461]
average_cost
0
f_measure
0.7979634973051618 [0.769231,0.967742,0.933333,0.233577,0.903226,0.592593,0.967742,0.967742,0.8,0.857143,0.827586,0.827586,0.933333,0.8125,0.787879,0.933333,1,0.4,0.424242,0.2,0.823529,0.933333,0.6875,0.896552,0.9375,0.533333,0.4,0.787879,0.903226,0.967742,0.967742,0.896552,0.933333,0.606061,0.777778,0.83871,0.4,0.4375,0.903226,0.866667,0.75,0.83871,0.967742,0.903226,0.933333,0.967742,0.875,0.909091,0.823529,0.857143,0.785714,0.774194,0.827586,0.733333,0.764706,0.533333,0.933333,0.9375,0.666667,0.6875,0.866667,0.903226,0.75,0.903226,0.875,0.625,0.533333,0.933333,0.787879,0.756757,0.6875,0.866667,0.705882,0.866667,0.787879,0.933333,0.896552,0.6,0.933333,0.866667,0.909091,0.866667,0.785714,0.62069,0.785714,0.896552,0.866667,0.787879,0.896552,0.967742,0.83871,0.785714,0.896552,0.903226,0.903226,0.866667,0.727273,0.823529,0.545455,0.9375]
kappa
0.7758838383838383
kb_relative_information_score
943.0141776326207
mean_absolute_error
0.01545987340992855
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
0.8328227410391779 [1,1,1,0.132231,0.933333,0.727273,1,1,0.857143,1,0.923077,0.923077,1,0.8125,0.764706,1,1,0.428571,0.411765,0.214286,0.777778,1,0.6875,1,0.9375,0.571429,0.368421,0.764706,0.933333,1,1,1,1,0.588235,0.7,0.866667,0.428571,0.4375,0.933333,0.928571,0.75,0.866667,1,0.933333,1,1,0.875,0.882353,0.777778,1,0.916667,0.8,0.923077,0.785714,0.722222,0.571429,1,0.9375,0.647059,0.6875,0.928571,0.933333,0.75,0.933333,0.875,0.625,0.571429,1,0.764706,0.666667,0.6875,0.928571,0.666667,0.928571,0.764706,1,1,0.642857,1,0.928571,0.882353,0.928571,0.916667,0.692308,0.916667,1,0.928571,0.764706,1,1,0.866667,0.916667,1,0.933333,0.933333,0.928571,0.705882,0.777778,0.529412,0.9375]
predictive_accuracy
0.778125
prior_entropy
6.6438561897747395
recall
0.778125 [0.625,0.9375,0.875,1,0.875,0.5,0.9375,0.9375,0.75,0.75,0.75,0.75,0.875,0.8125,0.8125,0.875,1,0.375,0.4375,0.1875,0.875,0.875,0.6875,0.8125,0.9375,0.5,0.4375,0.8125,0.875,0.9375,0.9375,0.8125,0.875,0.625,0.875,0.8125,0.375,0.4375,0.875,0.8125,0.75,0.8125,0.9375,0.875,0.875,0.9375,0.875,0.9375,0.875,0.75,0.6875,0.75,0.75,0.6875,0.8125,0.5,0.875,0.9375,0.6875,0.6875,0.8125,0.875,0.75,0.875,0.875,0.625,0.5,0.875,0.8125,0.875,0.6875,0.8125,0.75,0.8125,0.8125,0.875,0.8125,0.5625,0.875,0.8125,0.9375,0.8125,0.6875,0.5625,0.6875,0.8125,0.8125,0.8125,0.8125,0.9375,0.8125,0.6875,0.8125,0.875,0.875,0.8125,0.75,0.875,0.5625,0.9375]
relative_absolute_error
0.7808016873701275
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08103078546476461
root_relative_squared_error
0.8143900359224486
total_cost
0
area_under_roc_curve
0.9956539586816338 [0.987421,1,1,1,0.996835,0.981132,1,1,1,1,1,1,1,1,1,0.974843,1,0.981013,0.974684,1,1,1,1,1,0.962264,0.981013,0.990506,1,1,1,1,1,1,0.993671,0.996835,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,0.993711,1,1,1,1,1,0.993711,0.990506,1,1,1,0.981013,1,1,1,0.996835,1,1,1,0.993711,1,0.996835,0.920886,1,1,1,1,0.993711,1,1,1,1,1,0.993671,1,0.996835,1,1,0.996835,1,0.990506,1,0.993671,0.949367,1]
area_under_roc_curve
0.9936776032959159 [1,1,1,1,1,1,0.996835,1,1,1,1,0.996835,1,0.787975,0.996835,1,1,0.984177,0.993671,0.962264,1,1,1,1,1,0.996835,0.974684,1,1,1,1,1,1,0.996835,1,0.993711,0.974843,0.993711,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.974843,1,1,1,0.984177,1,1,1,1,1,0.993671,0.984177,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,0.993671,0.946203,0.993671,0.974843,0.993711,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.974843,0.993671,0.984177,0.987421]
area_under_roc_curve
0.996244327680917 [0.996835,1,1,1,1,0.974684,1,1,0.996835,1,1,0.996835,1,1,1,1,1,0.968354,1,0.93038,1,0.993711,0.977848,1,1,0.971519,1,1,1,1,1,1,1,0.993671,0.987421,1,0.993711,0.987421,1,1,0.993671,1,1,1,1,1,0.984177,1,0.993671,1,1,1,1,1,1,1,1,1,0.987421,0.968354,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,0.993711,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1]
area_under_roc_curve
0.997237232306345 [1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,0.990506,0.974843,0.984177,1,1,1,1,1,0.996835,0.984177,1,1,1,1,1,1,0.990506,1,0.993711,1,0.955975,1,1,0.996835,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,0.993671,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1,1,0.990506,0.987342,1,0.993711,1,1,1,1,0.993671,1,1,1,1,1,0.996835,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,0.937107,0.996835]
area_under_roc_curve
0.9952641111376481 [1,1,1,1,1,0.958861,1,0.996835,1,1,0.993671,1,1,0.830189,1,1,1,0.990506,0.993711,0.981013,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.984177,1,1,1,0.981013,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.981132,0.981132,0.996835,1,0.977848,0.984177,1,0.996835,1,1,0.996835,1,0.993711,1,1,1,1,1,0.996835,0.993671,1,1,1,0.993711,0.962025,1,1,1,1,1,1,1,0.987342,0.993671,1,1,1,1,1,1,1,1,1,0.993711,0.996835,1,0.996835,1,0.974843,0.996835]
area_under_roc_curve
0.9968371845394473 [0.977848,1,1,0.996835,1,0.987342,1,1,1,1,0.996835,1,0.987421,1,1,1,1,0.996835,1,0.977848,1,1,0.990506,1,1,0.993671,0.984177,1,1,1,1,1,1,0.977848,0.987421,1,1,0.996835,1,1,0.993671,1,1,1,1,1,0.996835,1,1,1,1,1,0.987342,1,0.996835,0.993671,1,1,0.996835,0.993711,0.996835,0.996835,0.993711,1,1,0.977848,1,1,0.993671,1,1,0.996835,1,1,0.996835,1,1,1,1,1,1,0.990506,0.993671,0.993671,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,0.993711,1,1]
area_under_roc_curve
0.9953797965926279 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987342,0.971519,0.981013,1,1,1,1,0.886792,0.962264,1,1,1,0.993671,1,1,1,1,1,0.968354,0.981013,1,0.993711,0.996835,0.990506,1,1,1,0.993671,1,0.993671,1,1,0.981132,1,1,1,1,1,1,1,0.993671,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,0.89557,1]
area_under_roc_curve
0.9971969289865455 [0.977848,1,1,1,1,0.993671,1,1,0.993711,1,1,1,1,1,1,1,1,0.987421,0.971519,0.977848,0.987342,1,1,1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,0.987342,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.993671,1,1,1,0.990506,1,1,0.990506,0.981132,1,1,1,1,1,0.968553,0.981132,1,1,0.990506,1,1,1,1,0.996835,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.9976320555688242 [0.993711,1,1,1,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,0.993711,1,0.955975,1,1,1,1,1,0.981132,0.993711,0.996835,1,1,1,1,0.993711,0.993711,0.996835,1,1,0.977848,1,1,0.993711,1,1,0.990506,1,1,1,1,0.993711,1,0.996835,1,1,0.987342,0.996835,0.987421,1,1,0.996835,0.996835,1,1,1,1,1,1,0.974684,1,1,1,1,0.987421,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.9970003881060423 [1,1,1,1,1,0.987421,1,1,1,0.987421,0.993711,1,1,0.949367,1,1,1,0.981132,0.993671,0.987421,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.993671,1,0.996835,1,1,1,1,0.996835,1,1,1,1,1,1,0.993711,0.993711,1,1,1,0.993671,1,0.987421,1,1,0.984177,1,1,1,0.984177,1,0.993711,1,0.990506,1,0.977848,1,0.981132,0.993711,0.993671,1,0.993671,1,1,1,1,1,1,0.981132,1,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1]
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
0.7915515199368337
kappa
0.7852603323727944
kappa
0.8105238226818774
kappa
0.7726106351900833
kappa
0.7471754760211741
kappa
0.7599210266535044
kappa
0.7598261900059253
kappa
0.7851840151634812
kappa
0.7788396982741598
kappa
0.7662388943731491
kb_relative_information_score
92.49252906741602
kb_relative_information_score
93.10363505241256
kb_relative_information_score
96.50274983535266
kb_relative_information_score
95.51270727826783
kb_relative_information_score
94.28007507819542
kb_relative_information_score
92.16524442634207
kb_relative_information_score
97.31071610933576
kb_relative_information_score
96.75185212383167
kb_relative_information_score
93.83440267314782
kb_relative_information_score
91.06026598831842
mean_absolute_error
0.015654354026013124
mean_absolute_error
0.015447712011505766
mean_absolute_error
0.015075879762043323
mean_absolute_error
0.01533975978651883
mean_absolute_error
0.015429291869006111
mean_absolute_error
0.015724464784370736
mean_absolute_error
0.014932028746295938
mean_absolute_error
0.015375955929818445
mean_absolute_error
0.0155478296170556
mean_absolute_error
0.0160714575666576
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
0.79375
predictive_accuracy
0.7875
predictive_accuracy
0.8125
predictive_accuracy
0.775
predictive_accuracy
0.75
predictive_accuracy
0.7625
predictive_accuracy
0.7625
predictive_accuracy
0.7875
predictive_accuracy
0.78125
predictive_accuracy
0.76875
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
0.79375 [0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0,0,1,1,1,1,0,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.7875 [1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0,0,0,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5,0.5,0,0,0.5,1,0.5,1,0.5,1,0.5,1,0.5,1,1,1,0,1,0.5,0]
recall
0.8125 [1,1,0.5,1,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,0,1,0,1,0,0,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0,0,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1]
recall
0.775 [0,1,0.5,1,0.5,0.5,1,1,0.5,0.5,1,0.5,1,1,1,1,1,0,0,0.5,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0,0.5,0,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,0,1,1,1,0.5,1,1,1,1,1,0,1,0,1]
recall
0.75 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,0.5,0.5,0,0,1,1,1,0,1,0.5,1,1,0.5,1,1,1,0,1,1,1,1,0,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0,0,0.5,1,0.5,0,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,0.5,0.5,1,0,1,1,1,1,1,1,1,0,0.5,1,1,1,0,1]
recall
0.7625 [0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,0,1,1,0.5,1,1,1,0.5,0.5,0.5,0.5,0.5,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0,1,0.5,0.5,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1]
recall
0.7625 [1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,0,0,1,1,1,0.5,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0,0.5,1,0,1,0,1,1,0,0.5,0.5,1,1,1,0,1,1,0,1,0.5,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,0,0,1,1,1,0,1,1,0.5,1,1,0,1]
recall
0.7875 [0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,0,1,0.5,0,0.5,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,0.5,1,1,0,1,1,1,1,0.5,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0,0.5,1,0,1,1,1,0,1,1,1,1,1,1,1,1]
recall
0.78125 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,0.5,0,1,0.5,1,1,0,0.5,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,1,0,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,0,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,0.5,0.5,1]
recall
0.76875 [1,1,1,1,1,0,0.5,1,1,0,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,0.5,1,0,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,1,0,0,0,0.5,0.5,0,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,0.5,1,0.5,0,1,1,1,1,1]
relative_absolute_error
0.7906239407077322
relative_absolute_error
0.7801874753285728
relative_absolute_error
0.7614080687900655
relative_absolute_error
0.7747353427534749
relative_absolute_error
0.7792571651013175
relative_absolute_error
0.7941648880995308
relative_absolute_error
0.754142865974541
relative_absolute_error
0.7765634307989102
relative_absolute_error
0.7852439200533118
relative_absolute_error
0.8116897760938169
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
0.08171227384988651
root_mean_squared_error
0.08118853941580637
root_mean_squared_error
0.07948478197560015
root_mean_squared_error
0.08078904005014208
root_mean_squared_error
0.08071954698326676
root_mean_squared_error
0.08236032572421356
root_mean_squared_error
0.07879276473201537
root_mean_squared_error
0.0804502918204782
root_mean_squared_error
0.08151295295574322
root_mean_squared_error
0.08320413749316637
root_relative_squared_error
0.8212392518995233
root_relative_squared_error
0.815975522785484
root_relative_squared_error
0.7988521162311192
root_relative_squared_error
0.8119604030888372
root_relative_squared_error
0.811261971487757
root_relative_squared_error
0.8277524182990064
root_relative_squared_error
0.7918970812449777
root_relative_squared_error
0.8085558552821942
root_relative_squared_error
0.8192360015396705
root_relative_squared_error
0.8362330456665897
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
3833.0040719999943
usercpu_time_millis
3604.257589000099
usercpu_time_millis
3603.1133110000155
usercpu_time_millis
3632.247396000025
usercpu_time_millis
3636.6487130001133
usercpu_time_millis
3637.4411349999036
usercpu_time_millis
3716.1359870000297
usercpu_time_millis
3631.0995520000233
usercpu_time_millis
3638.453367000011
usercpu_time_millis
3602.92670299998
usercpu_time_millis_testing
170.7984419999775
usercpu_time_millis_testing
169.51527100002295
usercpu_time_millis_testing
170.92457499995817
usercpu_time_millis_testing
171.47072900002058
usercpu_time_millis_testing
169.2921030000889
usercpu_time_millis_testing
175.76827699997466
usercpu_time_millis_testing
170.94214100006866
usercpu_time_millis_testing
170.88031200000842
usercpu_time_millis_testing
167.91920399998617
usercpu_time_millis_testing
167.1629899999516
usercpu_time_millis_training
3662.2056300000168
usercpu_time_millis_training
3434.742318000076
usercpu_time_millis_training
3432.1887360000574
usercpu_time_millis_training
3460.7766670000046
usercpu_time_millis_training
3467.3566100000244
usercpu_time_millis_training
3461.672857999929
usercpu_time_millis_training
3545.193845999961
usercpu_time_millis_training
3460.219240000015
usercpu_time_millis_training
3470.534163000025
usercpu_time_millis_training
3435.7637130000285