5963754
1
Jan van Rijn
9954
Supervised Classification
6970
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)
3998488
class_weight
null
6941
criterion
"gini"
6941
max_depth
9
6941
max_features
null
6941
max_leaf_nodes
null
6941
min_impurity_split
1e-07
6941
min_samples_leaf
1
6941
min_samples_split
2
6941
min_weight_fraction_leaf
0.0
6941
presort
false
6941
random_state
49853
6941
splitter
"best"
6941
axis
0
6947
categorical_features
[]
6947
copy
true
6947
fill_empty
0
6947
missing_values
"NaN"
6947
strategy
"mean"
6947
strategy_nominal
"most_frequent"
6947
verbose
0
6947
categorical_features
[]
6948
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
6948
handle_unknown
"ignore"
6948
n_values
"auto"
6948
sparse
false
6948
threshold
0.0
6949
algorithm
"SAMME"
6971
learning_rate
1.9813158925528012
6971
n_estimators
376
6971
random_state
2080
6971
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
12850909
description
https://api.openml.org/data/download/12850909/description.xml
-1
12850910
predictions
https://api.openml.org/data/download/12850910/predictions.arff
area_under_roc_curve
0.9925118371212127 [0.974905,0.999803,1,1,0.997277,0.996054,0.999014,0.998935,0.996804,1,0.997672,0.999566,0.99708,0.96658,1,0.997514,1,0.979916,0.973919,0.952612,0.993963,0.999053,0.995028,1,0.996291,0.966225,0.979048,0.996843,0.998501,0.999842,0.999882,1,0.999132,0.987887,0.998027,0.998658,0.991083,0.975931,0.999882,0.999921,0.994358,0.997633,1,0.993805,0.997238,0.99925,0.999448,0.999724,0.995699,0.99274,0.98473,0.995384,0.991241,0.969658,0.992266,0.973958,1,0.999487,0.992109,0.977825,0.998816,0.998935,0.992148,0.997593,0.999803,0.984651,0.970565,1,0.987058,0.9869,0.984414,0.998185,0.990412,0.999961,0.976484,0.998422,0.999566,0.985519,0.999921,0.999882,0.988952,0.996212,0.992464,0.995423,0.995265,0.999448,1,0.996252,0.995936,0.999803,0.998422,0.999645,0.996094,0.994476,0.983744,0.998224,0.985322,0.99203,0.946023,0.998816]
average_cost
0
f_measure
0.7941249661499039 [0.540541,0.848485,1,1,0.827586,0.785714,0.933333,0.967742,0.848485,0.933333,0.8,0.903226,0.875,0.740741,1,0.9375,0.933333,0.428571,0.32,0.071429,0.827586,0.967742,0.787879,1,0.875,0.5625,0.3125,0.833333,0.903226,0.933333,0.967742,1,0.9375,0.5,0.714286,0.83871,0.533333,0.322581,0.914286,0.896552,0.875,0.764706,0.933333,0.774194,0.9375,0.714286,0.896552,0.9375,0.810811,0.827586,0.8125,0.733333,0.8,0.758621,0.727273,0.410256,1,0.969697,0.742857,0.666667,0.9375,0.875,0.645161,0.882353,0.9375,0.666667,0.434783,0.967742,0.75,0.631579,0.685714,0.785714,0.625,0.967742,0.685714,0.875,0.933333,0.564103,0.967742,0.941176,0.875,0.774194,0.666667,0.705882,0.909091,0.83871,0.933333,0.733333,0.769231,0.9375,0.896552,0.903226,0.714286,0.857143,0.967742,0.83871,0.848485,0.705882,0.421053,0.941176]
kappa
0.7891414141414141
kb_relative_information_score
0.2806865708773306
mean_absolute_error
0.019799838344716863
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
0.8077474419024085 [0.47619,0.823529,1,1,0.923077,0.916667,1,1,0.823529,1,0.857143,0.933333,0.875,0.909091,1,0.9375,1,0.5,0.444444,0.083333,0.923077,1,0.764706,1,0.875,0.5625,0.3125,0.75,0.933333,1,1,1,0.9375,0.5,0.833333,0.866667,0.571429,0.333333,0.842105,1,0.875,0.722222,1,0.8,0.9375,0.576923,1,0.9375,0.714286,0.923077,0.8125,0.785714,0.857143,0.846154,0.705882,0.347826,1,0.941176,0.684211,0.647059,0.9375,0.875,0.666667,0.833333,0.9375,0.6,0.333333,1,0.75,0.545455,0.631579,0.916667,0.625,1,0.631579,0.875,1,0.478261,1,0.888889,0.875,0.8,0.647059,0.666667,0.882353,0.866667,1,0.785714,1,0.9375,1,0.933333,0.833333,0.789474,1,0.866667,0.823529,0.666667,0.363636,0.888889]
predictive_accuracy
0.79125
prior_entropy
6.6438561897747395
recall
0.79125 [0.625,0.875,1,1,0.75,0.6875,0.875,0.9375,0.875,0.875,0.75,0.875,0.875,0.625,1,0.9375,0.875,0.375,0.25,0.0625,0.75,0.9375,0.8125,1,0.875,0.5625,0.3125,0.9375,0.875,0.875,0.9375,1,0.9375,0.5,0.625,0.8125,0.5,0.3125,1,0.8125,0.875,0.8125,0.875,0.75,0.9375,0.9375,0.8125,0.9375,0.9375,0.75,0.8125,0.6875,0.75,0.6875,0.75,0.5,1,1,0.8125,0.6875,0.9375,0.875,0.625,0.9375,0.9375,0.75,0.625,0.9375,0.75,0.75,0.75,0.6875,0.625,0.9375,0.75,0.875,0.875,0.6875,0.9375,1,0.875,0.75,0.6875,0.75,0.9375,0.8125,0.875,0.6875,0.625,0.9375,0.8125,0.875,0.625,0.9375,0.9375,0.8125,0.875,0.75,0.5,1]
relative_absolute_error
0.9999918355917589
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.09949793136795444
root_relative_squared_error
0.9999918356485986
total_cost
0
area_under_roc_curve
0.9923396325929463 [0.949686,1,1,1,1,0.993711,1,1,1,1,0.996835,0.996835,0.993671,0.981013,1,0.962264,1,0.946203,0.987342,0.955975,0.949686,1,1,1,0.993711,1,0.968354,0.993671,1,1,1,1,0.996835,0.977848,1,0.993711,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.968354,1,1,1,0.993671,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.993671,0.96519,1,1,0.880503,0.977848,1,1,1,1,1,1,0.927215,1,1,1,1,0.987421,1,0.996835,0.990506,1,0.996835,0.984177,1,1,1,1,0.993671,0.996835,0.993671,1,0.990506,0.962025,1]
area_under_roc_curve
0.9929645828357614 [1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.841772,1,1,1,0.96519,0.987342,0.955975,1,0.993711,1,1,1,0.962025,0.981013,1,1,1,1,1,1,1,0.993671,0.993711,0.993711,0.974843,1,1,1,1,1,0.993671,1,1,1,1,0.996835,0.996835,1,0.984177,1,1,1,0.968553,1,1,1,0.952532,1,1,1,0.996835,1,0.990506,0.993671,1,1,1,0.993671,1,1,1,1,1,0.996835,0.993671,1,1,0.990506,0.990506,1,1,0.993671,1,1,1,0.984177,1,0.996835,1,0.996835,0.987342,1,1,0.981132,0.990506,0.962025,0.993711]
area_under_roc_curve
0.9948277406257462 [0.977848,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,0.981132,0.93038,1,1,1,1,1,0.955696,0.974684,0.981132,1,1,1,1,1,0.977848,1,1,0.993711,0.993711,1,1,1,0.981132,1,1,1,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,0.981132,0.987342,1,1,1,0.968553,1,0.993671,0.996835,1,0.937107,0.993671,0.996835,0.996835,1,1,0.987421,1,1,0.987342,1,1,1,1,1,0.996835,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,0.993671,0.949686,1]
area_under_roc_curve
0.9917442878751691 [0.958861,1,1,1,0.996835,0.984177,1,1,0.993671,1,1,1,0.993711,1,1,1,1,0.996835,0.81761,0.939873,0.993711,1,0.990506,1,0.987421,0.968354,0.987342,1,1,1,1,1,1,0.990506,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.89557,1,0.990506,1,1,1,0.984177,1,1,0.996835,1,1,0.990506,0.968354,1,1,0.987342,0.943038,0.996835,0.968553,1,1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,1,1,1,1,0.981132,1,1,1,0.949367,0.984177,1,1]
area_under_roc_curve
0.9927700322426558 [1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,0.981132,0.955975,1,1,1,0.993671,0.981132,0.958861,1,1,1,1,1,0.987342,0.955696,1,1,1,1,1,1,0.968354,0.993711,1,0.996835,0.93038,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.981013,1,1,0.984177,1,0.996835,1,1,1,1,0.987421,1,1,0.996835,0.993711,1,0.987342,0.996835,0.996835,1,1,1,0.949367,1,1,0.977848,1,1,1,1,1,0.990506,0.987421,1,1,0.987421,1,1,1,0.996835,1,0.987421,0.89557,0.993711,1,1,0.943396,1]
area_under_roc_curve
0.9934007045617385 [0.939873,1,1,1,1,0.996835,1,1,1,1,1,1,0.993711,0.993711,1,1,1,1,1,0.911392,1,1,0.996835,1,0.993671,0.958861,0.987342,1,1,1,1,1,1,0.993671,1,0.993671,0.984177,0.971519,1,1,0.974684,0.996835,1,1,0.993711,1,1,1,0.993671,0.981013,1,1,1,1,1,0.971519,1,1,0.993671,0.993711,1,1,0.987421,1,1,0.977848,1,1,0.984177,0.990506,0.993671,1,1,1,0.987342,1,1,0.990506,1,1,0.993711,1,1,0.993671,1,1,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.886792,1]
area_under_roc_curve
0.9910402237083034 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.977848,0.943038,0.993671,1,0.981013,1,0.996835,0.874214,1,0.993671,1,1,0.996835,1,1,0.993711,1,1,0.984177,0.971519,1,1,1,0.993671,1,1,0.968553,0.996835,1,1,1,1,0.874214,1,1,1,0.990506,0.987342,1,1,1,1,1,1,1,1,1,0.943396,0.886792,1,1,1,1,1,1,1,0.993671,1,1,0.993711,1,1,0.949367,1,1,1,1,1,1,0.993711,1,1,1,1,0.987342,1,1,1,0.977848,0.974843,0.816456,1]
area_under_roc_curve
0.9937268629090037 [0.990506,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.987421,0.981013,0.993671,1,1,1,1,0.987342,0.993711,0.949686,1,0.981132,1,1,1,1,1,0.990506,1,0.977848,0.977848,1,1,1,1,1,1,1,0.996835,1,0.996835,0.981132,0.981132,0.968553,0.996835,0.984177,0.993711,0.981013,0.952532,1,1,0.996835,0.91195,1,1,1,1,1,0.962264,0.880503,1,0.993671,0.996835,1,1,0.996835,1,0.993671,1,1,1,1,1,0.984177,1,0.971519,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.971519,1]
area_under_roc_curve
0.9952646087094974 [0.987421,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.968553,0.996835,0.924528,1,1,0.955975,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,0.993711,1,1,0.968354,1,1,1,1,0.993711,0.993711,0.987342,0.996835,1,0.962025,0.96519,0.924528,1,1,0.993671,0.984177,1,1,1,0.996835,1,1,0.987342,1,0.996835,0.993711,1,0.993711,0.968354,1,1,0.996835,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,1,0.993671,1]
area_under_roc_curve
0.9952596329910038 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,1,0.990506,0.968553,1,0.996835,1,1,1,0.987421,0.974843,1,0.993671,1,1,1,1,1,1,1,0.990506,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,0.993671,0.936709,1,1,1,1,0.987342,0.993671,1,0.996835,0.977848,1,1,0.993711,0.987342,1,1,0.993711,1,1,0.993671,1,0.936709,0.993671,1,1,1,1,1,0.974843,1,0.987421,0.996835,1,1,1,1,1,1,1,1,0.990506,1,0.996835,0.955975,0.996835,0.993671,1]
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
0.7409981048641819
kappa
0.7915762049500651
kappa
0.8230997038499506
kappa
0.78520097923083
kappa
0.8483472216737096
kappa
0.7663034896573504
kappa
0.7725387987205308
kappa
0.7725477807613331
kappa
0.7662758103359786
kappa
0.8230927183699257
kb_relative_information_score
0.028538747076459398
kb_relative_information_score
0.027197550712811814
kb_relative_information_score
0.028145160361501796
kb_relative_information_score
0.027693278600838447
kb_relative_information_score
0.028819607855404442
kb_relative_information_score
0.027800304310235658
kb_relative_information_score
0.028967861001155528
kb_relative_information_score
0.02675085118601815
kb_relative_information_score
0.028571488229792625
kb_relative_information_score
0.028201721543112738
mean_absolute_error
0.019799835633307925
mean_absolute_error
0.019799843363081104
mean_absolute_error
0.019799837904437696
mean_absolute_error
0.019799840507992748
mean_absolute_error
0.019799834018258716
mean_absolute_error
0.01979983989149993
mean_absolute_error
0.01979983316402431
mean_absolute_error
0.0197998459368265
mean_absolute_error
0.019799835447791514
mean_absolute_error
0.019799837579947955
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
0.74375
predictive_accuracy
0.79375
predictive_accuracy
0.825
predictive_accuracy
0.7875
predictive_accuracy
0.85
predictive_accuracy
0.76875
predictive_accuracy
0.775
predictive_accuracy
0.775
predictive_accuracy
0.76875
predictive_accuracy
0.825
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
0.74375 [0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,0.5,0,0,0,0,1,1,1,1,1,0.5,1,1,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,0,1,1,0,0.5,1,1,0.5,1,1,1,0.5,1,1,0,0.5,1,0.5,1,1,1,0.5,0.5,1,1,1,1,0,1,1,0.5,0.5,0.5,0,1,1,1,1,1,1,0.5,1,1,0.5,1]
recall
0.79375 [1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,0,0.5,1,0.5,1,0.5,0,1]
recall
0.825 [0.5,1,1,1,1,1,0,1,1,1,0.5,0.5,1,1,1,1,0.5,0.5,0,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,0,1]
recall
0.7875 [0.5,1,1,1,0.5,0,1,1,0.5,0.5,1,1,0,0.5,1,1,1,0,0,0,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,0.5,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,0.5,1,1]
recall
0.85 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0.5,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,0.5,1,1,1,1,1]
recall
0.76875 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,0,1,1,1,1,1,1,0.5,1,0,1,0.5,1,1,1,0.5,0,0,1,1,1,1,0.5,0.5,1,0.5,0,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1]
recall
0.775 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0.5,1,0.5,1,1,0,0,1,1,1,0.5,1,1,1,0.5,0.5,0.5,0,1,0,1,0,1,1,0,0.5,0.5,1,1,1,0,1,1,1,0.5,0.5,1,1,0.5,0,1,1,1,1,1,0,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0,1,1,1,1,0,0,1]
recall
0.775 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,0,1,1,1,0.5,1,0,1,0,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,0,0,0.5,0,1,0.5,0.5,1,1,1,0,1,0.5,1,1,1,0,0,1,1,1,1,0.5,0.5,1,0.5,1,1,0,1,1,1,1,0,1,1,0.5,1,0,0,1,1,1,1,1,1,1,0.5,1,1,1]
recall
0.76875 [1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0,0,1,1,1,1,1,1,1,0.5,1,1,0,1,0,0,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,0.5,0.5,0,0,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0,1,1,1,0,0,1,1,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1]
recall
0.825 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0,1,1,1,1,1,0,0,1,0.5,0,1,1,1,0,0.5,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,0,1,0,0.5,0.5,0.5,1,1,1,1,1,1,0,1,0,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1]
relative_absolute_error
0.9999916986519137
relative_absolute_error
0.9999920890444985
relative_absolute_error
0.9999918133554375
relative_absolute_error
0.9999919448481169
relative_absolute_error
0.9999916170837718
relative_absolute_error
0.9999919137121159
relative_absolute_error
0.9999915739406201
relative_absolute_error
0.9999922190316397
relative_absolute_error
0.999991689282398
relative_absolute_error
0.9999917969670667
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
0.09949791774273303
root_mean_squared_error
0.09949795658602979
root_mean_squared_error
0.09949792915527221
root_mean_squared_error
0.09949794223891681
root_mean_squared_error
0.09949790962667487
root_mean_squared_error
0.09949793914092671
root_mean_squared_error
0.09949790533419231
root_mean_squared_error
0.09949796951951513
root_mean_squared_error
0.09949791681045446
root_mean_squared_error
0.09949792752480974
root_relative_squared_error
0.9999916987099714
root_relative_squared_error
0.9999920890997926
root_relative_squared_error
0.9999918134103057
root_relative_squared_error
0.9999919449058816
root_relative_squared_error
0.9999916171405178
root_relative_squared_error
0.9999919137699096
root_relative_squared_error
0.9999915739994448
root_relative_squared_error
0.9999922190862109
root_relative_squared_error
0.9999916893402191
root_relative_squared_error
0.9999917970235415
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
12449.118618
usercpu_time_millis
12493.914987
usercpu_time_millis
12483.805630000003
usercpu_time_millis
12457.054198000002
usercpu_time_millis
12508.201932999988
usercpu_time_millis
12455.473888
usercpu_time_millis
12440.479230999985
usercpu_time_millis
12352.855793999992
usercpu_time_millis
12396.689464000005
usercpu_time_millis
12358.664188999996
usercpu_time_millis_testing
106.5747169999991
usercpu_time_millis_testing
106.32696100000061
usercpu_time_millis_testing
106.45267400000336
usercpu_time_millis_testing
103.85877000000221
usercpu_time_millis_testing
104.82001699999444
usercpu_time_millis_testing
105.83651800000382
usercpu_time_millis_testing
103.15017399999249
usercpu_time_millis_testing
102.60749799999758
usercpu_time_millis_testing
102.37033399999973
usercpu_time_millis_testing
104.40292399999862
usercpu_time_millis_training
12342.543901000001
usercpu_time_millis_training
12387.588026
usercpu_time_millis_training
12377.352955999999
usercpu_time_millis_training
12353.195427999999
usercpu_time_millis_training
12403.381915999993
usercpu_time_millis_training
12349.637369999997
usercpu_time_millis_training
12337.329056999992
usercpu_time_millis_training
12250.248295999994
usercpu_time_millis_training
12294.319130000005
usercpu_time_millis_training
12254.261264999997