5952979
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)
3987720
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
19825
6941
splitter
"best"
6941
axis
0
6947
categorical_features
[]
6947
copy
true
6947
fill_empty
0
6947
missing_values
"NaN"
6947
strategy
"most_frequent"
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.R"
6971
learning_rate
0.05129720397177312
6971
n_estimators
89
6971
random_state
38695
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
12829426
description
https://api.openml.org/data/download/12829426/description.xml
-1
12829425
predictions
https://api.openml.org/data/download/12829425/predictions.arff
area_under_roc_curve
0.9954498106060602 [0.995423,0.997199,1,1,0.999053,0.993213,0.999961,0.999961,0.998224,0.999882,0.997475,0.999921,0.995857,0.995778,1,0.999921,0.999842,0.979482,0.983744,0.985322,0.999448,0.999921,0.995186,1,0.999527,0.942235,0.990412,0.998501,0.997988,1,0.999329,1,1,0.98836,0.999803,0.996765,0.989702,0.986269,0.996173,0.999842,0.999487,0.999842,1,0.999842,0.999684,1,0.998501,0.999842,0.995975,0.993134,0.998895,0.996843,0.997988,0.964686,0.998658,0.969302,1,0.999684,0.995186,0.993529,0.999921,0.999014,0.994949,0.99925,0.999171,0.995541,0.990017,1,0.99566,0.996765,0.999605,0.99783,0.991319,0.999763,0.989781,0.999605,0.999961,0.987058,1,1,0.987058,0.996883,0.998343,0.993213,0.99708,0.998737,1,0.980548,0.999171,1,0.999487,0.998619,0.998106,0.999132,0.998777,0.999921,0.999211,0.997435,0.967961,0.99929]
average_cost
0
f_measure
0.8132663756011743 [0.666667,0.875,0.969697,1,0.9375,0.689655,0.9375,0.9375,0.8,0.967742,0.742857,0.969697,0.848485,0.733333,0.969697,0.967742,0.875,0.594595,0.545455,0.428571,0.857143,0.9375,0.75,1,0.882353,0.357143,0.514286,0.875,0.933333,0.969697,0.903226,1,1,0.451613,0.909091,0.8125,0.529412,0.37037,0.823529,0.969697,0.848485,0.933333,1,0.842105,0.909091,0.967742,0.774194,0.9375,0.75,0.903226,0.875,0.666667,0.774194,0.571429,0.787879,0.444444,1,0.9375,0.625,0.75,0.967742,0.8125,0.625,0.882353,0.875,0.65,0.363636,0.941176,0.62069,0.6,0.814815,0.83871,0.647059,0.9375,0.75,0.903226,0.969697,0.785714,1,1,0.774194,0.774194,0.848485,0.727273,0.774194,0.875,1,0.777778,0.909091,0.967742,0.875,0.848485,0.83871,0.896552,0.9375,0.967742,0.941176,0.666667,0.466667,0.875]
kappa
0.8125
kb_relative_information_score
1372.9838404394186
mean_absolute_error
0.004044616286898861
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
0.8180372480976191 [0.818182,0.875,0.941176,1,0.9375,0.769231,0.9375,0.9375,0.857143,1,0.684211,0.941176,0.823529,0.785714,0.941176,1,0.875,0.52381,0.529412,0.5,0.789474,0.9375,0.75,1,0.833333,0.416667,0.473684,0.875,1,0.941176,0.933333,1,1,0.466667,0.882353,0.8125,0.5,0.454545,0.777778,0.941176,0.823529,1,1,0.727273,0.882353,1,0.8,0.9375,0.75,0.933333,0.875,0.647059,0.8,0.666667,0.764706,0.4,1,0.9375,0.625,0.75,1,0.8125,0.625,0.833333,0.875,0.541667,0.352941,0.888889,0.692308,0.642857,1,0.866667,0.611111,0.9375,0.75,0.933333,0.941176,0.916667,1,1,0.8,0.8,0.823529,0.705882,0.8,0.875,1,0.7,0.882353,1,0.875,0.823529,0.866667,1,0.9375,1,0.888889,0.647059,0.5,0.875]
predictive_accuracy
0.814375
prior_entropy
6.6438561897747395
recall
0.814375 [0.5625,0.875,1,1,0.9375,0.625,0.9375,0.9375,0.75,0.9375,0.8125,1,0.875,0.6875,1,0.9375,0.875,0.6875,0.5625,0.375,0.9375,0.9375,0.75,1,0.9375,0.3125,0.5625,0.875,0.875,1,0.875,1,1,0.4375,0.9375,0.8125,0.5625,0.3125,0.875,1,0.875,0.875,1,1,0.9375,0.9375,0.75,0.9375,0.75,0.875,0.875,0.6875,0.75,0.5,0.8125,0.5,1,0.9375,0.625,0.75,0.9375,0.8125,0.625,0.9375,0.875,0.8125,0.375,1,0.5625,0.5625,0.6875,0.8125,0.6875,0.9375,0.75,0.875,1,0.6875,1,1,0.75,0.75,0.875,0.75,0.75,0.875,1,0.875,0.9375,0.9375,0.875,0.875,0.8125,0.8125,0.9375,0.9375,1,0.6875,0.4375,0.875]
relative_absolute_error
0.20427354984337642
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.05466908710110289
root_relative_squared_error
0.5494449986230797
total_cost
0
area_under_roc_curve
0.9957693953506886 [1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,1,1,0.911392,0.974684,1,1,1,0.974843,1,1,1,0.993671,1,0.996835,1,0.996835,1,1,0.974684,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,1,1,0.974684,1,0.993711,1,1,1,1,1,1,0.996835,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.996835,0.996835,1,1,1,1,1,1,1,1,1,0.914557,1]
area_under_roc_curve
0.9950200521455297 [0.993711,0.984177,1,1,1,1,1,1,1,1,1,1,0.987342,0.996835,1,1,1,0.96519,0.96519,0.993711,1,0.993711,1,1,1,0.882911,0.993671,1,1,1,1,1,1,1,1,1,0.949686,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.984177,1,0.996835,1,0.974843,1,1,1,0.962025,1,0.993711,1,0.996835,1,0.993671,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.990506,1,1,1,1,0.984177,1]
area_under_roc_curve
0.9964065361038135 [0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.958861,1,1,0.996835,1,1,0.920886,1,1,1,1,1,1,1,0.981013,1,1,0.993711,0.937107,1,1,1,1,1,1,0.996835,1,0.996835,1,0.984177,1,1,1,1,0.993671,1,1,1,1,0.981132,0.993671,1,1,0.996835,1,1,0.996835,1,1,1,0.996835,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.930818,1]
area_under_roc_curve
0.9968386772549958 [0.993671,0.990506,1,1,1,0.968354,1,1,0.996835,1,0.993671,1,1,1,1,1,1,1,0.968553,0.977848,1,1,1,1,1,0.984177,0.977848,1,1,1,1,1,1,0.990506,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.974684,1,0.993671,1,1,1,1,1,1,0.996835,1,0.996835,0.996835,0.984177,1,1,1,1,0.993671,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.990506,1,0.990506]
area_under_roc_curve
0.9946640394873022 [0.996835,1,1,1,1,1,1,1,1,1,1,1,0.968553,0.981132,1,1,1,0.996835,1,1,0.993671,1,1,1,1,0.974684,0.981013,1,1,1,1,1,1,0.968354,1,1,0.977848,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,0.993711,0.987342,0.943038,1,0.996835,1,1,1,1,1,1,1,1,0.987421,1,0.993671,0.990506,1,1,1,1,0.962025,1,1,0.927215,1,1,1,1,0.996835,0.996835,0.987421,1,1,1,1,1,1,0.981013,1,1,0.981013,1,0.996835,1,0.993711,0.996835]
area_under_roc_curve
0.996362252209219 [0.990506,1,1,1,1,0.987342,1,1,1,1,0.987342,1,1,1,1,1,1,0.993671,1,0.971519,1,1,1,1,1,0.977848,0.990506,1,1,1,1,1,1,0.993671,1,0.984177,0.996835,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.949367,1,1,1,1,1,0.990506,1,1,0.971519,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.990506,1,1,0.990506,1,1,0.987342,1,1,1,0.990506,1,0.993671,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.981132,0.987421,1]
area_under_roc_curve
0.9949603435236047 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.990506,1,1,1,1,1,0.735849,0.974843,0.984177,1,1,0.993671,1,1,0.987421,1,1,0.993671,0.993671,1,1,0.996835,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.996835,0.993671,1,1,0.996835,0.993711,1,1,1,0.990506,1,1,0.962264,1,0.996835,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,0.761006,1,1,1,1,0.984177,1,1,1,1,1,0.993671,1]
area_under_roc_curve
0.9954166169094817 [1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,0.968354,0.996835,1,1,1,1,1,0.962264,0.993711,1,0.974843,1,1,1,1,1,1,1,0.993671,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.990506,1,1,0.876582,1,1,1,0.993711,1,0.993671,1,1,1,0.974843,0.993711,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,1,1,1,0.911392,1,0.996835,1,0.987342,1,1,0.993711,1,1,1,1,1,0.993711,1,1,1,1,1,1]
area_under_roc_curve
0.9949038691187007 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,1,1,0.899371,0.993711,1,1,1,1,1,1,1,1,1,1,0.968354,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.993671,0.996835,0.863924,1,1,1,1,0.993671,1,1,1,0.993671,1,1,1,0.996835,1,0.993671,0.993711,1,0.993711,0.958861,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,0.993711,1,0.927215,1]
area_under_roc_curve
0.9971964314146963 [1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.990506,1,1,1,0.993711,0.993671,1,1,1,1,1,1,0.981132,1,1,1,1,1,1,1,1,0.996835,1,0.993671,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.952532,1,1,1,1,1,1,1,1,0.984177,1,1,1,0.990506,1,0.990506,0.993711,1,1,0.96519,1,0.996835,1,1,1,1,1,1,0.962264,1,0.949686,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
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
0.823127640254254
kappa
0.8041228970855383
kappa
0.867330016583748
kappa
0.7978202495656295
kappa
0.7535934291581109
kappa
0.7789095503178175
kappa
0.7978202495656295
kappa
0.8294310419710191
kappa
0.8420595435520809
kappa
0.8294377763739735
kb_relative_information_score
136.82735731658343
kb_relative_information_score
137.4492990283721
kb_relative_information_score
142.00579336587174
kb_relative_information_score
134.50272256672224
kb_relative_information_score
128.27017182523926
kb_relative_information_score
135.1563193208616
kb_relative_information_score
135.43831562185372
kb_relative_information_score
140.10778077494405
kb_relative_information_score
141.52639663421124
kb_relative_information_score
141.6996839847594
mean_absolute_error
0.00395671820134144
mean_absolute_error
0.004198451710098552
mean_absolute_error
0.0034565674045044952
mean_absolute_error
0.004221402534151296
mean_absolute_error
0.0049828137997169125
mean_absolute_error
0.00472360055069762
mean_absolute_error
0.004179721367208809
mean_absolute_error
0.0037969720143127992
mean_absolute_error
0.0035383776131083533
mean_absolute_error
0.003391537673848263
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
0.825
predictive_accuracy
0.80625
predictive_accuracy
0.86875
predictive_accuracy
0.8
predictive_accuracy
0.75625
predictive_accuracy
0.78125
predictive_accuracy
0.8
predictive_accuracy
0.83125
predictive_accuracy
0.84375
predictive_accuracy
0.83125
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
0.825 [1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,0.5,0,0.5,1,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,0.5,0.5,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1]
recall
0.80625 [0,0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0.5,0,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,0.5,1,0,1,1,1,0.5,1,0,0.5,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,1]
recall
0.86875 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,1,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1]
recall
0.8 [0.5,0.5,1,1,0.5,0,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,0.5,1,1,1,0,0.5,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0.5,1,0.5]
recall
0.75625 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,1,0,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,0,0.5,0,1,1,1,0,0.5,1,0,1,0.5,1,0,1,0,0.5,1,1,1,0,0.5,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5]
recall
0.78125 [0.5,1,1,1,1,0,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,0.5,0,1,1,1,1,1,0,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,0.5,1,1,0,1,1,1,1,1,1,0.5,1,1,0.5,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0,0,1]
recall
0.8 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0,1,1,0,0.5,0,1,1,1,0,1,1,1,1,0.5,1,0.5,0,1,1,1,1,0.5,1,1,0,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,0,1]
recall
0.83125 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,0,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,1]
recall
0.84375 [1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,0,1,0,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1]
recall
0.83125 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,0.5,0,1,1,1,0,1,0.5,1,0,1,0,1,0,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1]
relative_absolute_error
0.1998342525930017
relative_absolute_error
0.21204301566154268
relative_absolute_error
0.1745741113386106
relative_absolute_error
0.21320214818945907
relative_absolute_error
0.25165726261196486
relative_absolute_error
0.23856568437866726
relative_absolute_error
0.21109703874791932
relative_absolute_error
0.19176626334913094
relative_absolute_error
0.17870594005597712
relative_absolute_error
0.17128978150748772
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
0.054225043583729794
root_mean_squared_error
0.05453461165101118
root_mean_squared_error
0.04836170197295034
root_mean_squared_error
0.057012043387459986
root_mean_squared_error
0.06286885752778913
root_mean_squared_error
0.058905323686615836
root_mean_squared_error
0.057058786747592405
root_mean_squared_error
0.05280638641734175
root_mean_squared_error
0.04904016573344613
root_mean_squared_error
0.0501198210686995
root_relative_squared_error
0.5449821933572732
root_relative_squared_error
0.5480934694974181
root_relative_squared_error
0.486053392931111
root_relative_squared_error
0.5729925952959616
root_relative_squared_error
0.6318557921757182
root_relative_squared_error
0.5920207782513306
root_relative_squared_error
0.5734623837414152
root_relative_squared_error
0.5307241523661885
root_relative_squared_error
0.4928722102869234
root_relative_squared_error
0.5037231546806834
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
4299.254372
usercpu_time_millis
4186.832433999998
usercpu_time_millis
4358.453965999999
usercpu_time_millis
4310.128438000002
usercpu_time_millis
4320.119408
usercpu_time_millis
4156.417622999999
usercpu_time_millis
4137.375312000003
usercpu_time_millis
4111.088239999994
usercpu_time_millis
4102.029762000001
usercpu_time_millis
4131.4536020000005
usercpu_time_millis_testing
101.28393400000047
usercpu_time_millis_testing
94.45227799999856
usercpu_time_millis_testing
100.45763399999963
usercpu_time_millis_testing
95.6747809999996
usercpu_time_millis_testing
94.5296159999991
usercpu_time_millis_testing
94.4591680000002
usercpu_time_millis_testing
94.05422500000071
usercpu_time_millis_testing
94.57766199999895
usercpu_time_millis_testing
95.75905799999873
usercpu_time_millis_testing
95.17050200000199
usercpu_time_millis_training
4197.970438
usercpu_time_millis_training
4092.3801559999997
usercpu_time_millis_training
4257.996332
usercpu_time_millis_training
4214.453657000002
usercpu_time_millis_training
4225.589792000001
usercpu_time_millis_training
4061.958454999999
usercpu_time_millis_training
4043.3210870000025
usercpu_time_millis_training
4016.510577999995
usercpu_time_millis_training
4006.2707040000023
usercpu_time_millis_training
4036.2830999999987