10017169
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)
7941890
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
"gini"
8919
max_depth
null
8919
max_features
0.15549324447266943
8919
max_leaf_nodes
null
8919
min_impurity_decrease
0.0
8919
min_impurity_split
null
8919
min_samples_leaf
8
8919
min_samples_split
4
8919
min_weight_fraction_leaf
0.0
8919
n_estimators
100
8919
n_jobs
null
8919
oob_score
false
8919
random_state
56287
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
20954861
description
https://api.openml.org/data/download/20954861/description.xml
-1
20954862
predictions
https://api.openml.org/data/download/20954862/predictions.arff
area_under_roc_curve
0.996445707070707 [0.992661,0.999803,1,1,1,0.996252,0.999645,0.999448,0.997317,0.999842,0.998777,1,0.999842,0.991477,1,0.999211,1,0.989149,0.991517,0.977904,0.999842,1,0.997475,1,0.998658,0.974116,0.987295,0.999369,0.998382,0.999921,0.999882,0.999961,0.999369,0.99057,0.999684,0.99779,0.994042,0.989781,0.999329,0.999803,0.999763,0.999487,0.999211,0.99929,0.999527,0.999882,0.998619,0.998658,0.99783,0.996804,0.999329,0.996291,0.99779,0.987176,0.998895,0.980824,1,0.999645,0.993016,0.987295,1,0.999724,0.993924,0.998146,0.998422,0.996015,0.985993,0.999763,0.994713,0.99349,0.995344,0.998737,0.995896,1,0.985401,0.99925,0.999724,0.994634,0.999921,0.999803,0.997238,0.997633,0.998619,0.995699,0.997001,0.996883,1,0.995423,0.998461,1,0.999921,0.999053,0.999014,0.994949,0.999645,0.998777,0.997159,0.996607,0.966343,0.999803]
average_cost
0
f_measure
0.7905904842600348 [0.615385,0.820513,1,1,1,0.785714,0.9375,0.882353,0.848485,0.9375,0.8,0.967742,0.941176,0.714286,0.969697,0.933333,0.864865,0.580645,0.578947,0.296296,0.909091,0.969697,0.8,1,0.864865,0.434783,0.3125,0.83871,0.848485,0.941176,0.909091,0.896552,0.848485,0.666667,0.909091,0.833333,0.533333,0.540541,0.909091,0.969697,0.789474,0.875,0.736842,0.8,0.848485,0.933333,0.8125,0.827586,0.705882,0.727273,0.914286,0.705882,0.8,0.592593,0.75,0.37037,0.969697,0.866667,0.533333,0.571429,0.969697,0.842105,0.62069,0.857143,0.857143,0.756757,0.5625,0.888889,0.434783,0.642857,0.692308,0.83871,0.666667,1,0.785714,0.903226,0.909091,0.72,0.969697,0.933333,0.666667,0.777778,0.727273,0.571429,0.8,0.83871,0.969697,0.722222,0.823529,1,0.914286,0.857143,0.875,0.692308,0.848485,0.882353,0.787879,0.64,0.363636,0.909091]
kappa
0.7960858585858586
kb_relative_information_score
1136.7201214090624
mean_absolute_error
0.013675211252841357
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
0.8038617913491208 [0.8,0.695652,1,1,1,0.916667,0.9375,0.833333,0.823529,0.9375,0.736842,1,0.888889,0.833333,0.941176,1,0.761905,0.6,0.5,0.363636,0.882353,0.941176,0.857143,1,0.761905,0.714286,0.3125,0.866667,0.823529,0.888889,0.882353,1,0.823529,0.647059,0.882353,0.75,0.571429,0.47619,0.882353,0.941176,0.681818,0.875,0.636364,0.736842,0.823529,1,0.8125,0.923077,0.666667,0.705882,0.842105,0.666667,0.736842,0.727273,0.75,0.454545,0.941176,0.928571,0.571429,0.666667,0.941176,0.727273,0.692308,0.789474,0.789474,0.666667,0.5625,0.8,0.714286,0.75,0.9,0.866667,0.6,1,0.916667,0.933333,0.882353,1,0.941176,1,1,0.7,0.705882,0.526316,0.857143,0.866667,0.941176,0.65,0.777778,1,0.842105,0.789474,0.875,0.9,0.823529,0.833333,0.764706,0.888889,0.666667,0.882353]
predictive_accuracy
0.798125
prior_entropy
6.6438561897747395
recall
0.798125 [0.5,1,1,1,1,0.6875,0.9375,0.9375,0.875,0.9375,0.875,0.9375,1,0.625,1,0.875,1,0.5625,0.6875,0.25,0.9375,1,0.75,1,1,0.3125,0.3125,0.8125,0.875,1,0.9375,0.8125,0.875,0.6875,0.9375,0.9375,0.5,0.625,0.9375,1,0.9375,0.875,0.875,0.875,0.875,0.875,0.8125,0.75,0.75,0.75,1,0.75,0.875,0.5,0.75,0.3125,1,0.8125,0.5,0.5,1,1,0.5625,0.9375,0.9375,0.875,0.5625,1,0.3125,0.5625,0.5625,0.8125,0.75,1,0.6875,0.875,0.9375,0.5625,1,0.875,0.5,0.875,0.75,0.625,0.75,0.8125,1,0.8125,0.875,1,1,0.9375,0.875,0.5625,0.875,0.9375,0.8125,0.5,0.25,0.9375]
relative_absolute_error
0.6906672349919865
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.07401787037426845
root_relative_squared_error
0.7439075873109425
total_cost
0
area_under_roc_curve
0.9959278719847147 [0.993711,1,1,1,1,1,1,1,0.993671,1,0.996835,1,1,0.993671,1,0.993711,1,0.990506,0.981013,0.993711,1,1,1,1,1,0.993671,0.977848,1,1,1,0.996835,1,0.996835,0.990506,1,0.987421,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.987421,0.987342,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.993671,0.993671,1,0.996835,0.993671,1,1,1,1,1,1,0.996835,1,1,0.857595,1]
area_under_roc_curve
0.9969983978186449 [0.987421,1,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,1,1,0.984177,0.996835,0.981132,1,1,1,1,1,0.96519,0.984177,1,1,1,1,1,1,1,1,0.993711,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.977848,1,1,1,0.943396,1,1,1,0.958861,1,1,1,0.996835,1,1,1,1,0.987421,1,0.996835,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.993671,0.996835,1]
area_under_roc_curve
0.9968013593662924 [1,1,1,1,1,0.996835,1,1,0.996835,1,1,1,1,1,1,1,1,0.987342,0.987421,0.946203,1,1,0.996835,1,1,0.990506,1,1,1,1,1,1,1,0.977848,1,1,0.993711,0.974843,1,1,0.996835,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.981132,0.962025,1,1,1,1,1,1,1,1,0.981132,0.996835,1,1,0.993711,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,1,0.949686,1]
area_under_roc_curve
0.9968389260409205 [1,1,1,1,1,0.993671,1,1,0.987342,1,1,1,1,1,1,1,1,1,0.968553,0.987342,1,1,1,1,1,0.984177,0.974684,1,1,1,1,1,1,0.987342,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.949367,1,0.990506,1,1,1,1,1,1,0.996835,1,0.996835,1,0.962025,0.996835,1,1,0.996835,1,0.981132,1,0.993711,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,0.996835,1,0.987421,0.996835,1,0.993711,1]
area_under_roc_curve
0.9948250039805748 [1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,1,0.943396,1,1,1,0.993671,1,0.987342,1,1,1,1,1,0.977848,0.990506,1,1,1,1,1,1,0.987342,1,1,0.993671,0.955696,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.996835,1,0.993671,0.981013,1,0.996835,1,1,1,1,0.993711,1,0.993671,1,0.968553,1,0.984177,0.949367,1,1,0.993711,1,0.93038,1,1,0.987342,1,1,1,1,1,0.993671,0.987421,0.993711,1,1,1,1,1,0.993671,1,0.968553,0.996835,1,0.996835,1,0.993711,1]
area_under_roc_curve
0.9957318286760609 [0.949367,1,1,1,1,0.990506,1,1,1,1,0.993671,1,1,1,1,1,1,0.996835,1,0.936709,1,1,0.996835,1,1,1,0.974684,1,1,1,1,1,0.990506,0.993671,1,0.993671,0.987342,1,1,1,1,0.996835,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,0.990506,1,1,0.993671,1,1,1,0.987421,1,1,0.993671,1,1,0.996835,1,0.987342,1,1,1,0.971519,0.993711,1,1,1,1,1,0.996835,1,0.993671,1,1,1,0.993711,0.996835,1,1,0.996835,1,1,1,1,1,0.987421,0.937107,1]
area_under_roc_curve
0.9971611038133904 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.977848,1,1,0.996835,1,0.993671,0.880503,0.987421,0.996835,1,1,1,1,1,0.981132,1,1,0.996835,1,1,1,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,1,1,1,1,0.996835,1,1,1,0.990506,0.993711,1,1,1,1,1,1,0.968553,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.962264,1,1,1,1,0.993671,1,1,1,1,1,0.96519,1]
area_under_roc_curve
0.9964876403152614 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.987421,0.981013,0.996835,1,1,1,1,1,0.993711,0.993711,1,0.968553,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835,0.993671,1,1,0.955696,1,1,0.993671,0.987421,1,1,1,1,1,0.937107,0.981132,1,0.993671,0.990506,1,1,1,1,0.993671,1,1,0.993711,1,1,0.993671,0.993711,0.993671,0.996835,0.996835,0.987342,1,0.981132,0.993711,1,1,1,1,0.987421,1,1,0.987342,1,0.981013,1]
area_under_roc_curve
0.9979084567311519 [0.987421,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.962264,0.993711,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,1,1,0.996835,0.996835,1,1,1,1,0.993711,1,1,0.993671,1,0.971519,1,0.981132,1,1,0.996835,0.996835,1,1,0.996835,1,1,1,0.996835,1,0.996835,1,1,0.987421,0.990506,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,1,0.993671,1]
area_under_roc_curve
0.9975544343603214 [1,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.993671,1,1,1,0.993711,0.996835,0.987421,1,1,1,1,1,0.930818,0.987421,1,0.996835,1,1,1,1,1,0.996835,0.993671,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.987342,1,1,1,1,0.996835,1,1,1,0.977848,0.990506,0.993711,1,0.996835,1,0.996835,1,1,1,0.993671,1,1,0.996835,1,1,1,0.996835,1,0.981132,1,0.974843,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1]
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
0.7789357334596557
kappa
0.8357484107869073
kappa
0.8420844848006317
kappa
0.759958940345059
kappa
0.7472753119570368
kappa
0.7788746298124383
kappa
0.8294243070362474
kappa
0.7852179406190777
kappa
0.8230927183699257
kappa
0.7789095503178175
kb_relative_information_score
112.71827995139594
kb_relative_information_score
114.71807236168563
kb_relative_information_score
113.85956883215393
kb_relative_information_score
113.25387229357192
kb_relative_information_score
113.0408894286251
kb_relative_information_score
111.61341180476609
kb_relative_information_score
115.88265012883316
kb_relative_information_score
113.43593724854145
kb_relative_information_score
114.21581688124058
kb_relative_information_score
113.98162247824713
mean_absolute_error
0.013741528228409669
mean_absolute_error
0.013518652229714725
mean_absolute_error
0.01363539765442891
mean_absolute_error
0.013779095104752271
mean_absolute_error
0.013637368917540787
mean_absolute_error
0.013961786145941476
mean_absolute_error
0.013357147636064912
mean_absolute_error
0.013607307531010656
mean_absolute_error
0.013740891211219335
mean_absolute_error
0.013772937869330953
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
0.78125
predictive_accuracy
0.8375
predictive_accuracy
0.84375
predictive_accuracy
0.7625
predictive_accuracy
0.75
predictive_accuracy
0.78125
predictive_accuracy
0.83125
predictive_accuracy
0.7875
predictive_accuracy
0.825
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
0.78125 [1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,0,1,1,0.5,0,1,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,0,0,0.5,1,0.5,1,1,1,0.5,0.5,1,1,0,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,1]
recall
0.8375 [0,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,0.5,1,1,0.5,0.5,1,0,0,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1]
recall
0.84375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,0,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1]
recall
0.7625 [0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5,0.5,0,0.5]
recall
0.75 [0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,0,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,0,0.5,0,1,1,0.5,1,1,1,0,1,0.5,1,0,1,0,0.5,0.5,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,0,0.5,1,1,0,0,1]
recall
0.78125 [0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,0,0,1,1,1,1,0.5,1,1,0.5,0.5,1,1,1,1,0.5,0,1,1,1,1,0,1,0.5,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1]
recall
0.83125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,0.5,0,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0,1,1,1,1,1,0,1]
recall
0.7875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,1,0.5,1,1,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,0,1,1,1,1,1,0,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,0,1,0,0.5,1,0,1,1,1,1,1,0,1,1,0,0,0,1]
recall
0.825 [0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,0.5,1,0.5,1,1,1,0,1,1,0.5,1,0.5,1,0,1,0,0.5,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1]
recall
0.78125 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,0.5,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,0.5,1,0,1,0.5,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,0,0.5,1,1,0.5,1,0.5,0,0,1,0.5,1,0,0,1,1,1,0.5,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0.5,1,1]
relative_absolute_error
0.6940165771924065
relative_absolute_error
0.6827602136219546
relative_absolute_error
0.6886564471933783
relative_absolute_error
0.6959138941794065
relative_absolute_error
0.6887560059364022
relative_absolute_error
0.7051407144414875
relative_absolute_error
0.6746034159628732
relative_absolute_error
0.6872377540914463
relative_absolute_error
0.6939844046070359
relative_absolute_error
0.6956029226934813
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
0.07441517896694312
root_mean_squared_error
0.07314497648379777
root_mean_squared_error
0.07370837284801889
root_mean_squared_error
0.07454835741823866
root_mean_squared_error
0.07406972500183784
root_mean_squared_error
0.07547700306003911
root_mean_squared_error
0.07236871963403912
root_mean_squared_error
0.07377008988991061
root_mean_squared_error
0.07398797974227621
root_mean_squared_error
0.07464370058692484
root_relative_squared_error
0.7479006889105974
root_relative_squared_error
0.735134673624625
root_relative_squared_error
0.7407970201348428
root_relative_squared_error
0.7492391827078941
root_relative_squared_error
0.7444287459269769
root_relative_squared_error
0.7585724225777453
root_relative_squared_error
0.7273329987410108
root_relative_squared_error
0.7414172997443141
root_relative_squared_error
0.7436071751562007
root_relative_squared_error
0.7501974176074566
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
882.3004760001822
usercpu_time_millis
853.5912979996283
usercpu_time_millis
856.1967240002559
usercpu_time_millis
847.3307510002996
usercpu_time_millis
856.5602880003098
usercpu_time_millis
855.7320129998516
usercpu_time_millis
853.4849259999646
usercpu_time_millis
855.8612710003217
usercpu_time_millis
846.5444919993388
usercpu_time_millis
853.1488579997131
usercpu_time_millis_testing
41.64375400023346
usercpu_time_millis_testing
42.08481599971492
usercpu_time_millis_testing
41.91300599995884
usercpu_time_millis_testing
41.62584200003039
usercpu_time_millis_testing
41.82303400011733
usercpu_time_millis_testing
41.95153099999516
usercpu_time_millis_testing
44.604791999972804
usercpu_time_millis_testing
42.243417000008776
usercpu_time_millis_testing
41.61633699959566
usercpu_time_millis_testing
42.25414399979854
usercpu_time_millis_training
840.6567219999488
usercpu_time_millis_training
811.5064819999134
usercpu_time_millis_training
814.2837180002971
usercpu_time_millis_training
805.7049090002693
usercpu_time_millis_training
814.7372540001925
usercpu_time_millis_training
813.7804819998564
usercpu_time_millis_training
808.8801339999918
usercpu_time_millis_training
813.617854000313
usercpu_time_millis_training
804.9281549997431
usercpu_time_millis_training
810.8947139999145