8968833
1
Jan van Rijn
9954
Supervised Classification
8330
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)(2)
6904415
axis
0
7104
categorical_features
[]
7104
copy
true
7104
fill_empty
0
7104
missing_values
"NaN"
7104
strategy
"most_frequent"
7104
strategy_nominal
"most_frequent"
7104
verbose
0
7104
categorical_features
[]
7105
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
7105
handle_unknown
"ignore"
7105
n_values
"auto"
7105
sparse
true
7105
threshold
0.0
7106
C
2064.6597352124663
7107
cache_size
200
7107
class_weight
null
7107
coef0
0.13712099561045776
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decision_function_shape
null
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degree
3
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gamma
1.026296001662096
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kernel
"poly"
7107
max_iter
-1
7107
probability
true
7107
random_state
57348
7107
shrinking
true
7107
tol
1.804601743526651e-05
7107
verbose
false
7107
copy
true
7713
with_mean
false
7713
with_std
true
7713
openml-pimp
openml-python
Sklearn_0.18.1.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
18852826
description
https://api.openml.org/data/download/18852826/description.xml
-1
18852827
predictions
https://api.openml.org/data/download/18852827/predictions.arff
area_under_roc_curve
0.9962389520202025 [0.987413,0.999763,1,0.999724,0.998422,0.994081,0.999724,0.999014,0.99712,0.999961,0.998658,0.999645,0.99929,0.96733,0.998974,0.999487,1,0.992622,0.983586,0.974432,0.997711,0.999684,0.995541,0.999171,0.998816,0.977667,0.990767,0.999053,0.99858,1,0.999605,0.999961,1,0.989978,0.998422,0.998698,0.993332,0.982521,0.999408,0.999842,0.998698,0.997948,1,0.998777,0.996528,0.999961,0.999092,0.999053,0.997475,0.996883,0.994831,0.998974,0.99783,0.99274,0.996173,0.993332,1,0.999329,0.992819,0.996646,0.998067,0.999408,0.997593,0.999171,0.999171,0.993647,0.986387,0.999882,0.998027,0.994081,0.996725,0.998974,0.99566,0.997948,0.989544,0.999448,0.999369,0.991122,0.999724,1,0.995936,0.997001,0.99783,0.995818,0.999369,0.999961,0.999961,0.999171,0.99925,0.999921,0.999408,0.999605,0.998224,0.997672,0.995936,0.997277,0.997869,0.998422,0.969736,0.996488]
average_cost
0
f_measure
0.8193982344247973 [0.666667,0.9375,0.967742,0.896552,0.896552,0.8,0.969697,0.8,0.83871,0.896552,0.848485,0.866667,0.909091,0.714286,0.896552,0.903226,0.967742,0.625,0.424242,0.266667,0.774194,0.9375,0.787879,0.909091,0.848485,0.411765,0.588235,0.848485,0.903226,0.967742,0.9375,0.967742,0.9375,0.5625,0.875,0.875,0.5625,0.533333,0.903226,0.896552,0.742857,0.875,0.967742,0.848485,0.866667,0.941176,0.866667,0.875,0.742857,0.8125,0.848485,0.8,0.83871,0.727273,0.733333,0.588235,0.967742,0.9375,0.666667,0.764706,0.896552,0.857143,0.709677,0.903226,0.909091,0.666667,0.484848,0.9375,0.83871,0.727273,0.787879,0.896552,0.75,0.933333,0.731707,0.9375,0.903226,0.685714,1,1,0.8125,0.8125,0.83871,0.533333,0.875,0.933333,0.967742,0.827586,0.903226,0.967742,0.848485,0.903226,0.866667,0.875,0.909091,0.8,0.8,0.83871,0.606061,0.933333]
kappa
0.8150252525252526
kb_relative_information_score
979.6439792290299
mean_absolute_error
0.015943433906295233
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
0.8281434270475191 [0.6,0.9375,1,1,1,0.857143,0.941176,0.736842,0.866667,1,0.823529,0.928571,0.882353,0.833333,1,0.933333,1,0.625,0.411765,0.285714,0.8,0.9375,0.764706,0.882353,0.823529,0.388889,0.555556,0.823529,0.933333,1,0.9375,1,0.9375,0.5625,0.875,0.875,0.5625,0.571429,0.933333,1,0.684211,0.875,1,0.823529,0.928571,0.888889,0.928571,0.875,0.684211,0.8125,0.823529,0.857143,0.866667,0.705882,0.785714,0.555556,1,0.9375,0.565217,0.722222,1,0.789474,0.733333,0.933333,0.882353,0.565217,0.470588,0.9375,0.866667,0.705882,0.764706,1,0.75,1,0.6,0.9375,0.933333,0.631579,1,1,0.8125,0.8125,0.866667,0.571429,0.875,1,1,0.923077,0.933333,1,0.823529,0.933333,0.928571,0.875,0.882353,0.736842,0.736842,0.866667,0.588235,1]
predictive_accuracy
0.816875
prior_entropy
6.6438561897747395
recall
0.816875 [0.75,0.9375,0.9375,0.8125,0.8125,0.75,1,0.875,0.8125,0.8125,0.875,0.8125,0.9375,0.625,0.8125,0.875,0.9375,0.625,0.4375,0.25,0.75,0.9375,0.8125,0.9375,0.875,0.4375,0.625,0.875,0.875,0.9375,0.9375,0.9375,0.9375,0.5625,0.875,0.875,0.5625,0.5,0.875,0.8125,0.8125,0.875,0.9375,0.875,0.8125,1,0.8125,0.875,0.8125,0.8125,0.875,0.75,0.8125,0.75,0.6875,0.625,0.9375,0.9375,0.8125,0.8125,0.8125,0.9375,0.6875,0.875,0.9375,0.8125,0.5,0.9375,0.8125,0.75,0.8125,0.8125,0.75,0.875,0.9375,0.9375,0.875,0.75,1,1,0.8125,0.8125,0.8125,0.5,0.875,0.875,0.9375,0.75,0.875,0.9375,0.875,0.875,0.8125,0.875,0.9375,0.875,0.875,0.8125,0.625,0.875]
relative_absolute_error
0.8052239346613739
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08228214781023498
root_relative_squared_error
0.8269667007003405
total_cost
0
area_under_roc_curve
0.9966023306265426 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.987342,1,0.981132,1,0.996835,0.981013,0.987421,0.993711,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.971519,1,1,0.962264,0.996835,1,1,1,1,1,1,0.949367,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,0.993711,0.993671,0.949367,1]
area_under_roc_curve
0.9961271495103894 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.889241,1,1,1,0.984177,0.993671,0.987421,1,0.993711,0.993711,1,1,0.977848,0.971519,1,1,1,1,1,1,0.996835,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.987421,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.974684,0.996835,1,0.981132,0.996835,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,1,0.974843]
area_under_roc_curve
0.9968777366451713 [1,1,1,1,1,1,0.993711,1,0.996835,1,1,1,1,1,0.996835,1,1,0.996835,1,0.943038,1,1,0.993671,1,1,0.943038,0.996835,0.993711,1,1,1,1,1,0.984177,1,1,1,1,0.993711,1,0.996835,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.981013,1,1,1,0.987421,1,0.993671,1,1,1,0.987342,1,0.996835,1,1,1,1,1,0.996835,0.993671,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1]
area_under_roc_curve
0.9971969289865455 [1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,1,1,1,0.990506,0.943396,0.977848,1,1,0.996835,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.962264,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,1,1,1,0.977848,0.981013,1,0.987421,1,0.993711,1,1,0.996835,1,1,1,1,0.993671,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.996835,1,1,1]
area_under_roc_curve
0.9962099952233101 [1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.918239,1,1,1,0.993671,0.987421,0.990506,1,1,1,1,1,0.990506,0.987342,1,1,1,1,1,1,0.981013,1,1,0.996835,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.993671,0.987342,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.949367,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.968354,0.993711,1,1,0.937107,1]
area_under_roc_curve
0.9960848559031925 [0.920886,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,0.962025,1,1,1,1,0.993671,0.987342,0.981013,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.984177,0.996835,1,0.993671,1,1,0.993711,1,1,1,1,0.990506,0.993671,1,1,0.993671,1,1,0.987342,1,1,0.993671,1,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,0.993711,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835]
area_under_roc_curve
0.9955783277605287 [1,1,1,1,1,1,1,1,0.993711,1,1,1,0.996835,1,1,1,1,1,0.996835,0.971519,0.996835,1,0.993671,1,1,0.880503,0.962264,0.993671,1,1,0.996835,1,1,1,1,1,0.974684,0.993671,1,1,1,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.977848,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.879747,1]
area_under_roc_curve
0.9972006607754158 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.993711,0.993671,0.990506,0.996835,1,1,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.990506,1,1,1,1,1,1,0.993711,1,1,0.993671,1,0.987421,0.962264,1,0.996835,1,1,0.990506,1,1,0.996835,1,1,1,1,1,1,0.974843,0.924528,1,1,0.996835,1,1,1,1,1,1,1,0.968553,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.993671]
area_under_roc_curve
0.9969988953904941 [1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,0.993671,0.974843,0.996835,1,0.974843,0.993671,1,0.981132,0.993711,1,1,1,1,1,1,0.993711,1,1,1,0.952532,1,1,1,1,1,0.996835,1,1,1,0.993671,0.993711,1,0.993671,1,1,0.974684,0.981013,0.993711,1,1,1,1,0.996835,1,1,1,1,1,0.984177,1,1,1,1,0.993711,0.984177,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1]
area_under_roc_curve
0.9975905083194017 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.96519,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.993671,0.996835,0.993711,0.993711,0.987342,1,1,1,1,1,0.990506,0.990506,1,0.990506,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.996835,1,1,0.996835,1,1,1,1,1,0.993711,1,1,1]
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
0.7852518553608084
kappa
0.7915021323645554
kappa
0.8547062539481995
kappa
0.7978521794061908
kappa
0.8230857323381905
kappa
0.8231066887783305
kappa
0.8041615667074664
kappa
0.8420345944238212
kappa
0.8231206569804169
kappa
0.8042079501046067
kb_relative_information_score
98.9559328297693
kb_relative_information_score
96.30699413018965
kb_relative_information_score
97.35356652191038
kb_relative_information_score
96.35430283964143
kb_relative_information_score
97.43631593917519
kb_relative_information_score
97.42120871601551
kb_relative_information_score
101.29129089365824
kb_relative_information_score
97.98447088150635
kb_relative_information_score
98.32406328718017
kb_relative_information_score
98.21583318998202
mean_absolute_error
0.01576705134589903
mean_absolute_error
0.016183986384904803
mean_absolute_error
0.016099635317663914
mean_absolute_error
0.016211040191425094
mean_absolute_error
0.01610725435574494
mean_absolute_error
0.015953120605310865
mean_absolute_error
0.015456333643631252
mean_absolute_error
0.015996728940292866
mean_absolute_error
0.015842202808220813
mean_absolute_error
0.01581698546985883
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
0.7875
predictive_accuracy
0.79375
predictive_accuracy
0.85625
predictive_accuracy
0.8
predictive_accuracy
0.825
predictive_accuracy
0.825
predictive_accuracy
0.80625
predictive_accuracy
0.84375
predictive_accuracy
0.825
predictive_accuracy
0.80625
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
0.7875 [1,0.5,1,0,0.5,1,1,1,0.5,1,0.5,0.5,1,0,1,0,1,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.79375 [1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0,0.5,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0]
recall
0.85625 [1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1]
recall
0.8 [0,1,1,0,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,1,1,0,1,1,1,0.5,0.5,1,1]
recall
0.825 [1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1]
recall
0.825 [0.5,1,0.5,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1]
recall
0.80625 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.84375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5]
recall
0.825 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0,0,1,0,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1]
recall
0.80625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.7963157245403536
relative_absolute_error
0.8173730497426654
relative_absolute_error
0.8131128948315094
relative_absolute_error
0.8187394036073266
relative_absolute_error
0.8134976947345914
relative_absolute_error
0.8057131618843857
relative_absolute_error
0.7806229112945064
relative_absolute_error
0.8079156030450929
relative_absolute_error
0.8001112529404437
relative_absolute_error
0.798837649992869
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
0.08157966943284205
root_mean_squared_error
0.08318915193956225
root_mean_squared_error
0.08272397613873658
root_mean_squared_error
0.08337319296035962
root_mean_squared_error
0.08279796333940447
root_mean_squared_error
0.08246687978226984
root_mean_squared_error
0.08026243040452657
root_mean_squared_error
0.08224025472941747
root_mean_squared_error
0.082037692637619
root_mean_squared_error
0.08210667600730498
root_relative_squared_error
0.8199065273635224
root_relative_squared_error
0.8360824351860424
root_relative_squared_error
0.8314072424803095
root_relative_squared_error
0.8379321170406451
root_relative_squared_error
0.8321508418254734
root_relative_squared_error
0.8288233268761651
root_relative_squared_error
0.8066677770115989
root_relative_squared_error
0.8265456593961477
root_relative_squared_error
0.8245098337741927
root_relative_squared_error
0.8252031427257771
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
1913.6134200016386
usercpu_time_millis
1742.0758549997117
usercpu_time_millis
1748.2255610011634
usercpu_time_millis
1750.9059780022653
usercpu_time_millis
1836.8458800032386
usercpu_time_millis
1767.7727059999597
usercpu_time_millis
1780.0892509985715
usercpu_time_millis
1755.1949750013591
usercpu_time_millis
1772.1086769997783
usercpu_time_millis
1759.183017999021
usercpu_time_millis_testing
151.42110899978434
usercpu_time_millis_testing
148.54686499893432
usercpu_time_millis_testing
149.8296140016464
usercpu_time_millis_testing
149.98141800242593
usercpu_time_millis_testing
148.42105800198624
usercpu_time_millis_testing
153.04166300120414
usercpu_time_millis_testing
149.19494499918073
usercpu_time_millis_testing
148.75772500090534
usercpu_time_millis_testing
148.21269800086156
usercpu_time_millis_testing
147.62075799808372
usercpu_time_millis_training
1762.1923110018543
usercpu_time_millis_training
1593.5289900007774
usercpu_time_millis_training
1598.395946999517
usercpu_time_millis_training
1600.9245599998394
usercpu_time_millis_training
1688.4248220012523
usercpu_time_millis_training
1614.7310429987556
usercpu_time_millis_training
1630.8943059993908
usercpu_time_millis_training
1606.4372500004538
usercpu_time_millis_training
1623.8959789989167
usercpu_time_millis_training
1611.5622600009374