10015042
6892
Scikit-learn Bot
9954
Supervised Classification
8918
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)
7939763
n_jobs
null
8891
remainder
"passthrough"
8891
sparse_threshold
0.3
8891
transformer_weights
null
8891
memory
null
8892
axis
0
8893
copy
true
8893
missing_values
"NaN"
8893
strategy
"mean"
8893
verbose
0
8893
copy
true
8894
with_mean
true
8894
with_std
true
8894
memory
null
8895
copy
true
8896
fill_value
-1
8896
missing_values
NaN
8896
strategy
"constant"
8896
verbose
0
8896
categorical_features
null
8897
categories
null
8897
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
8897
handle_unknown
"ignore"
8897
n_values
null
8897
sparse
true
8897
threshold
0.0
8898
memory
null
8918
bootstrap
false
8919
class_weight
null
8919
criterion
"entropy"
8919
max_depth
null
8919
max_features
0.025990570524802692
8919
max_leaf_nodes
null
8919
min_impurity_decrease
0.0
8919
min_impurity_split
null
8919
min_samples_leaf
5
8919
min_samples_split
11
8919
min_weight_fraction_leaf
0.0
8919
n_estimators
100
8919
n_jobs
null
8919
oob_score
false
8919
random_state
64473
8919
verbose
0
8919
warm_start
false
8919
openml-python
Sklearn_0.20.2.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
20950607
description
https://api.openml.org/data/download/20950607/description.xml
-1
20950608
predictions
https://api.openml.org/data/download/20950608/predictions.arff
area_under_roc_curve
0.9961726641414146 [0.991951,0.999763,1,1,0.999171,0.993805,0.999803,0.999014,0.995896,0.999842,0.99854,0.999961,0.999961,0.986506,1,0.999053,1,0.986506,0.990215,0.977943,0.998935,0.999645,0.994634,1,0.998619,0.980074,0.993726,0.999961,0.998146,0.999211,1,1,0.999842,0.989741,0.999605,0.998106,0.994042,0.982323,0.999408,0.999882,0.99712,0.999092,0.999882,0.996054,0.999132,0.993292,0.998974,0.998698,0.998264,0.998264,0.996449,0.989978,0.993411,0.994003,0.994752,0.98761,1,0.999763,0.993056,0.98911,0.998935,0.999842,0.991398,0.999921,0.998935,0.995344,0.988123,1,0.998303,0.992779,0.998895,0.99783,0.996252,0.999369,0.975339,0.999803,0.999921,0.992069,1,0.999369,0.992977,0.996686,0.996686,0.99566,0.995028,0.999369,1,0.997593,0.997356,0.999132,1,0.999842,0.999684,0.995778,0.999724,0.999645,0.994673,0.996409,0.978496,0.999369]
average_cost
0
f_measure
0.8061637069962757 [0.72,0.9375,0.969697,1,0.875,0.727273,0.9375,0.909091,0.827586,0.969697,0.75,0.9375,1,0.666667,0.969697,0.866667,0.888889,0.413793,0.5,0.384615,0.702703,0.967742,0.774194,1,0.823529,0.347826,0.571429,0.914286,0.933333,0.810811,0.888889,1,1,0.666667,0.9375,0.75,0.631579,0.296296,0.833333,0.914286,0.787879,0.882353,0.969697,0.848485,0.875,0.933333,0.8125,0.866667,0.857143,0.875,0.827586,0.56,0.875,0.666667,0.689655,0.518519,1,0.941176,0.62069,0.64,0.941176,0.941176,0.538462,0.903226,0.888889,0.774194,0.606061,0.969697,0.823529,0.733333,0.909091,0.764706,0.666667,0.875,0.56,0.903226,0.820513,0.692308,0.969697,0.967742,0.62069,0.727273,0.903226,0.634146,0.75,0.909091,0.941176,0.787879,0.848485,0.909091,0.941176,0.888889,0.9375,0.875,0.9375,0.882353,0.709677,0.764706,0.454545,0.882353]
kappa
0.8118686868686869
kb_relative_information_score
918.5237824881976
mean_absolute_error
0.016736558499367435
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
0.8167635558504547 [1,0.9375,0.941176,1,0.875,0.705882,0.9375,0.882353,0.923077,0.941176,0.75,0.9375,1,0.818182,0.941176,0.928571,0.8,0.461538,0.45,0.5,0.619048,1,0.8,1,0.777778,0.571429,0.666667,0.842105,1,0.714286,0.8,1,1,0.647059,0.9375,0.625,0.545455,0.363636,0.75,0.842105,0.764706,0.833333,0.941176,0.823529,0.875,1,0.8125,0.928571,0.789474,0.875,0.923077,0.777778,0.875,0.714286,0.769231,0.636364,1,0.888889,0.692308,0.888889,0.888889,0.888889,0.7,0.933333,0.8,0.8,0.588235,0.941176,0.777778,0.785714,0.882353,0.722222,0.565217,0.875,0.777778,0.933333,0.695652,0.9,0.941176,1,0.692308,0.705882,0.933333,0.52,0.75,0.882353,0.888889,0.764706,0.823529,0.882353,0.888889,0.8,0.9375,0.875,0.9375,0.833333,0.733333,0.722222,0.833333,0.833333]
predictive_accuracy
0.81375
prior_entropy
6.6438561897747395
recall
0.81375 [0.5625,0.9375,1,1,0.875,0.75,0.9375,0.9375,0.75,1,0.75,0.9375,1,0.5625,1,0.8125,1,0.375,0.5625,0.3125,0.8125,0.9375,0.75,1,0.875,0.25,0.5,1,0.875,0.9375,1,1,1,0.6875,0.9375,0.9375,0.75,0.25,0.9375,1,0.8125,0.9375,1,0.875,0.875,0.875,0.8125,0.8125,0.9375,0.875,0.75,0.4375,0.875,0.625,0.625,0.4375,1,1,0.5625,0.5,1,1,0.4375,0.875,1,0.75,0.625,1,0.875,0.6875,0.9375,0.8125,0.8125,0.875,0.4375,0.875,1,0.5625,1,0.9375,0.5625,0.75,0.875,0.8125,0.75,0.9375,1,0.8125,0.875,0.9375,1,1,0.9375,0.875,0.9375,0.9375,0.6875,0.8125,0.3125,0.9375]
relative_absolute_error
0.8452807322912831
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08602367802753776
root_relative_squared_error
0.8645704942535835
total_cost
0
area_under_roc_curve
0.9957333213916091 [0.987421,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,0.984177,1,1,1,0.987342,0.987342,1,1,1,1,1,0.987421,0.987342,0.993671,1,1,0.993711,1,1,0.996835,0.984177,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.996835,1,1,0.996835,0.993711,1,1,1,1,0.993671,1,1,0.996835,1,1,0.996835,0.993671,1,1,0.943396,1,1,1,1,1,1,1,0.955696,1,1,1,1,0.993711,1,0.981013,1,1,1,0.990506,1,1,1,1,0.996835,1,1,0.981132,0.993671,0.927215,1]
area_under_roc_curve
0.9966426339463418 [0.987421,1,1,1,1,1,1,1,1,1,1,1,1,0.939873,1,1,1,0.990506,0.993671,0.937107,1,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.990506,1,1,1,1,0.996835,1,1,0.968354,1,0.993671,1,0.981132,1,1,1,0.987342,1,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,0.984177,1,1,0.993671,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.993671,0.996835,1,0.981132,0.990506,1,1]
area_under_roc_curve
0.9966421363744924 [1,1,1,1,1,0.993671,1,1,0.996835,1,0.996835,1,1,0.993671,1,1,1,0.993671,1,0.962025,1,1,0.971519,1,1,0.990506,1,1,1,1,1,1,1,0.968354,1,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.952532,1,1,1,1,1,1,1,1,0.974843,0.990506,1,0.996835,0.993711,1,0.968553,1,1,0.993671,1,1,1,1,0.990506,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.962264,1]
area_under_roc_curve
0.9964050433882654 [1,1,1,1,0.996835,0.984177,1,1,0.987342,1,0.993671,1,1,1,1,1,1,0.987342,0.918239,0.990506,1,1,1,1,1,0.955696,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,0.968354,1,1,1,1,1,1,1,1,1,0.981013,0.977848,1,1,0.990506,1,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,0.996835,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.987342,0.990506,0.993711,1]
area_under_roc_curve
0.9949847245442239 [0.993671,1,1,1,1,1,1,1,0.987421,1,1,1,1,0.924528,1,1,1,0.996835,0.987421,0.993671,0.996835,1,0.996835,1,1,0.968354,0.993671,1,1,1,1,1,1,0.990506,1,1,0.996835,0.977848,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.987421,0.981013,1,0.968354,0.996835,1,0.996835,1,1,1,1,0.981132,1,1,1,0.993711,1,1,0.996835,0.996835,1,1,0.993711,0.876582,1,1,1,1,1,0.993711,1,1,0.990506,1,1,1,1,1,1,1,0.996835,1,0.987421,1,1,1,1,0.955975,1]
area_under_roc_curve
0.9964826645967679 [0.971519,1,1,1,1,0.971519,1,1,1,1,0.996835,1,1,0.993711,1,1,1,0.984177,1,0.968354,0.996835,0.996835,0.996835,1,1,1,0.987342,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.968354,1,1,0.996835,1,1,0.993711,1,1,1,0.996835,1,1,1,1,1,1,0.993671,0.987342,1,1,0.987342,1,1,1,0.993711,1,1,0.990506,1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.996835,0.981132,0.968553,0.996835]
area_under_roc_curve
0.9975526928588485 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.955975,1,0.993671,1,1,1,1,1,0.968553,0.993711,1,1,1,1,1,1,0.993711,1,1,0.984177,0.984177,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,0.993711,0.977848,1,1,0.996835,0.993671,1,1,0.987342,1,1,1,1,1,0.996835,1,0.993711,1,1,0.996835,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,0.977848,1]
area_under_roc_curve
0.9970401938539923 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993671,1,1,1,1,1,1,0.993711,1,0.993711,0.996835,1,1,1,1,1,1,0.993671,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.993671,1,0.993711,1,0.977848,1,1,0.990506,1,1,1,1,1,1,0.962264,0.918239,1,1,0.996835,1,1,1,1,0.984177,1,1,0.993711,1,1,0.977848,1,1,1,0.990506,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.977848,1]
area_under_roc_curve
0.9952633647798741 [1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,0.996835,1,1,1,0.974843,1,0.893082,1,1,1,1,1,1,0.993711,1,0.996835,1,1,1,1,0.993711,1,1,0.987342,0.96519,1,1,0.987421,1,1,0.993671,1,0.93038,1,1,0.993711,1,0.990506,0.993671,0.996835,0.977848,0.996835,0.962264,1,1,0.996835,0.981013,0.993671,1,1,1,1,1,0.996835,1,1,0.993711,1,0.987421,0.990506,1,1,1,1,1,1,0.996835,0.993671,0.993711,1,1,1,1,1,0.987342,1,1,1,1,1,1,1,0.996835,0.968553,1,0.996835,1]
area_under_roc_curve
0.9981846091075548 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835,0.987421,1,1,1,1,0.996835,0.987421,0.993711,1,0.993671,1,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835,0.996835,0.996835,0.996835,1,1,1,1,0.987342,1,1,1,0.981013,1,1,1,0.993671,1,1,1,1,0.993711,0.987342,1,1,0.993671,1,1,1,1,1,0.962264,1,0.987421,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1]
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
0.7852942337293286
kappa
0.8357743476372824
kappa
0.8294445102451735
kappa
0.8294781716270625
kappa
0.8104639684106614
kappa
0.8231346229767075
kappa
0.8104714522624971
kappa
0.8168107702633345
kappa
0.7978521794061908
kappa
0.7789270064348032
kb_relative_information_score
90.87406394356621
kb_relative_information_score
91.78916606432136
kb_relative_information_score
92.15925543317714
kb_relative_information_score
91.92467006178987
kb_relative_information_score
90.90650612622215
kb_relative_information_score
91.78410762289168
kb_relative_information_score
93.25995151464191
kb_relative_information_score
92.17046964999471
kb_relative_information_score
91.43409591479059
kb_relative_information_score
92.22149615680257
mean_absolute_error
0.016784155672253272
mean_absolute_error
0.016743818788593115
mean_absolute_error
0.016717186056220544
mean_absolute_error
0.01678078173782897
mean_absolute_error
0.016791020222670393
mean_absolute_error
0.016756197609224314
mean_absolute_error
0.01659197508169803
mean_absolute_error
0.016687618966402674
mean_absolute_error
0.016749609917889798
mean_absolute_error
0.01676322094089295
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
0.8375
predictive_accuracy
0.83125
predictive_accuracy
0.83125
predictive_accuracy
0.8125
predictive_accuracy
0.825
predictive_accuracy
0.8125
predictive_accuracy
0.81875
predictive_accuracy
0.8
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
0.7875 [1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,0.5,1,1,1,1,1,0,0,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,0,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,0,1,1,0,1,1,0.5,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0,1]
recall
0.8375 [1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,0,1,1,0,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1]
recall
0.83125 [0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0,1,1,1,0,1,0,1,1,0.5,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1]
recall
0.83125 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5]
recall
0.8125 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0,0.5,1,0,0.5,1,1,0.5,1,1,1,0,1,1,1,0,1,1,0.5,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,1]
recall
0.825 [0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0,1,0.5,0.5,0.5,0.5,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,0,1]
recall
0.8125 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0,0.5,0,0.5,1,1,1,0,1,1,0.5,0,1,1,0,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,0.5,1,0,1]
recall
0.81875 [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,0.5,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0,1,1,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,1,1,0.5,1,1,0,1,1,0,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1]
recall
0.8 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,0,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,0.5,0.5,1,1,1,1,0.5,0,0.5,0.5,1,1,1,1,0.5,0,1,1,0.5,1,1,1,0.5,1,1,0,1,0,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0,0.5,0.5,1]
recall
0.78125 [1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0.5,1,1,1,1,1,0.5,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,0,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0.5,1,0,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,1,1,0,0,1,0,0,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1]
relative_absolute_error
0.84768462991178
relative_absolute_error
0.8456474135653075
relative_absolute_error
0.8443023260717432
relative_absolute_error
0.8475142291832799
relative_absolute_error
0.8480313243772912
relative_absolute_error
0.8462726065264792
relative_absolute_error
0.8379785394796971
relative_absolute_error
0.8428090387072044
relative_absolute_error
0.8459398948429177
relative_absolute_error
0.8466273202471173
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
0.08626584936522821
root_mean_squared_error
0.08601958610637646
root_mean_squared_error
0.08592036420959716
root_mean_squared_error
0.08620808668905389
root_mean_squared_error
0.08621614913191981
root_mean_squared_error
0.08616341319974559
root_mean_squared_error
0.08543821601444251
root_mean_squared_error
0.0857776477838832
root_mean_squared_error
0.08612366020040946
root_mean_squared_error
0.0861004416364478
root_relative_squared_error
0.867004407775092
root_relative_squared_error
0.8645293688985421
root_relative_squared_error
0.8635321513148927
root_relative_squared_error
0.8664238710364341
root_relative_squared_error
0.8665049016360704
root_relative_squared_error
0.8659748855754902
root_relative_squared_error
0.8586863796279988
root_relative_squared_error
0.8620977972678813
root_relative_squared_error
0.8655753528994623
root_relative_squared_error
0.8653419975514873
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
556.1663019989282
usercpu_time_millis
545.7897440001034
usercpu_time_millis
531.9234159996995
usercpu_time_millis
537.8636389996245
usercpu_time_millis
530.2874249991874
usercpu_time_millis
531.8622669992692
usercpu_time_millis
532.0883829990635
usercpu_time_millis
533.0508280012509
usercpu_time_millis
532.3963689997981
usercpu_time_millis
530.4377799993745
usercpu_time_millis_testing
41.408096999475674
usercpu_time_millis_testing
41.53244899953279
usercpu_time_millis_testing
40.933696999672975
usercpu_time_millis_testing
40.855635999832884
usercpu_time_millis_testing
41.02368099938758
usercpu_time_millis_testing
41.60891699939384
usercpu_time_millis_testing
41.19714899934479
usercpu_time_millis_testing
41.290189000392274
usercpu_time_millis_testing
41.11322500011738
usercpu_time_millis_testing
41.34369599978527
usercpu_time_millis_training
514.7582049994526
usercpu_time_millis_training
504.2572950005706
usercpu_time_millis_training
490.9897190000265
usercpu_time_millis_training
497.00800299979164
usercpu_time_millis_training
489.2637439997998
usercpu_time_millis_training
490.25334999987535
usercpu_time_millis_training
490.8912339997187
usercpu_time_millis_training
491.76063900085865
usercpu_time_millis_training
491.28314399968076
usercpu_time_millis_training
489.0940839995892