6007225
1
Jan van Rijn
9954
Supervised Classification
6969
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
4041939
bootstrap
false
6902
class_weight
null
6902
criterion
"gini"
6902
max_depth
null
6902
max_features
0.4175265032157014
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
6
6902
min_samples_split
2
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
55692
6902
verbose
0
6902
warm_start
false
6902
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
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
12937569
description
https://api.openml.org/data/download/12937569/description.xml
-1
12937570
predictions
https://api.openml.org/data/download/12937570/predictions.arff
area_under_roc_curve
0.9947259706439395 [0.989149,0.999645,0.999842,1,0.999645,0.996528,0.998737,0.999645,0.99858,0.999882,0.997356,0.999527,1,0.986821,0.999961,0.998816,0.999842,0.981929,0.991083,0.984296,0.999803,1,0.996094,1,0.998106,0.963029,0.984099,0.998343,0.996765,1,0.999527,0.998126,0.995936,0.986506,0.999329,0.99708,0.991398,0.982599,0.99779,0.999408,0.99929,0.999132,0.998303,0.998185,0.998895,0.999566,0.997514,0.998185,0.99562,0.993016,0.997633,0.993647,0.996843,0.982915,0.996686,0.978141,0.999487,0.99858,0.991083,0.960997,1,0.99925,0.991734,0.995265,0.998382,0.995226,0.982994,0.999092,0.981337,0.990017,0.992227,0.997514,0.995462,1,0.979956,0.999053,0.999408,0.991556,0.99925,0.998698,0.99491,0.995305,0.998224,0.994634,0.996488,0.996409,0.999566,0.991122,0.998816,0.999842,0.999487,0.99858,0.997317,0.993884,0.996291,0.996962,0.997751,0.993095,0.969184,0.999369]
average_cost
0
f_measure
0.7427430532944764 [0.689655,0.864865,0.969697,0.967742,0.914286,0.764706,0.823529,0.903226,0.827586,0.9375,0.8125,0.903226,0.8,0.642857,0.941176,0.827586,0.888889,0.5,0.451613,0.357143,0.823529,0.9375,0.705882,0.969697,0.787879,0.272727,0.3125,0.727273,0.848485,0.969697,0.848485,0.866667,0.875,0.466667,0.941176,0.666667,0.484848,0.444444,0.848485,0.903226,0.864865,0.941176,0.742857,0.8,0.848485,0.903226,0.774194,0.8125,0.647059,0.705882,0.823529,0.645161,0.645161,0.592593,0.666667,0.4,0.969697,0.8,0.432432,0.4,1,0.8,0.625,0.6875,0.857143,0.684211,0.424242,0.705882,0.434783,0.62069,0.56,0.8125,0.645161,0.969697,0.642857,0.857143,0.823529,0.769231,0.9375,0.827586,0.709677,0.666667,0.727273,0.516129,0.628571,0.714286,0.933333,0.529412,0.823529,0.9375,0.810811,0.857143,0.83871,0.666667,0.866667,0.6875,0.6875,0.740741,0.454545,0.888889]
kappa
0.7462121212121212
kb_relative_information_score
1190.1097913603003
mean_absolute_error
0.012157903932178638
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
0.7532785592030943 [0.769231,0.761905,0.941176,1,0.842105,0.722222,0.777778,0.933333,0.923077,0.9375,0.8125,0.933333,0.736842,0.75,0.888889,0.923077,0.8,0.5,0.466667,0.416667,0.777778,0.9375,0.666667,0.941176,0.764706,0.5,0.3125,0.705882,0.823529,0.941176,0.823529,0.928571,0.875,0.5,0.888889,0.6,0.470588,0.4,0.823529,0.933333,0.761905,0.888889,0.684211,0.736842,0.823529,0.933333,0.8,0.8125,0.611111,0.666667,0.777778,0.666667,0.666667,0.727273,0.647059,0.368421,0.941176,0.857143,0.380952,0.555556,1,0.666667,0.625,0.6875,0.789474,0.590909,0.411765,0.666667,0.714286,0.692308,0.777778,0.8125,0.666667,0.941176,0.75,0.789474,0.777778,1,0.9375,0.923077,0.733333,0.714286,0.705882,0.533333,0.578947,0.833333,1,0.5,0.777778,0.9375,0.714286,0.789474,0.866667,0.818182,0.928571,0.6875,0.6875,0.909091,0.833333,0.8]
predictive_accuracy
0.74875
prior_entropy
6.6438561897747395
recall
0.74875 [0.625,1,1,0.9375,1,0.8125,0.875,0.875,0.75,0.9375,0.8125,0.875,0.875,0.5625,1,0.75,1,0.5,0.4375,0.3125,0.875,0.9375,0.75,1,0.8125,0.1875,0.3125,0.75,0.875,1,0.875,0.8125,0.875,0.4375,1,0.75,0.5,0.5,0.875,0.875,1,1,0.8125,0.875,0.875,0.875,0.75,0.8125,0.6875,0.75,0.875,0.625,0.625,0.5,0.6875,0.4375,1,0.75,0.5,0.3125,1,1,0.625,0.6875,0.9375,0.8125,0.4375,0.75,0.3125,0.5625,0.4375,0.8125,0.625,1,0.5625,0.9375,0.875,0.625,0.9375,0.75,0.6875,0.625,0.75,0.5,0.6875,0.625,0.875,0.5625,0.875,0.9375,0.9375,0.9375,0.8125,0.5625,0.8125,0.6875,0.6875,0.625,0.3125,1]
relative_absolute_error
0.6140355521302331
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.06980363063085773
root_relative_squared_error
0.7015528842639821
total_cost
0
area_under_roc_curve
0.994188360799299 [1,0.996835,1,1,1,1,1,1,0.993671,1,0.990506,1,1,0.996835,1,0.987421,1,0.962025,0.990506,0.993711,1,1,0.993711,1,0.993711,0.993671,0.974684,1,0.990506,1,0.996835,1,0.990506,0.968354,1,0.981132,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.958861,0.996835,1,0.987421,0.996835,1,1,1,1,0.993711,1,1,1,1,0.990506,1,0.993671,0.984177,1,1,1,0.984177,1,0.993671,1,1,1,1,0.993671,1,1,1,1,0.993711,1,0.984177,0.993671,1,1,0.993671,1,1,1,1,1,1,0.987342,1,0.993671,0.886076,1]
area_under_roc_curve
0.9917998915293371 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.977848,1,1,1,0.971519,1,0.993711,1,1,1,1,1,0.943038,0.993671,0.990506,1,1,1,1,1,0.996835,1,0.993711,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,0.974684,1,1,1,0.949686,1,1,1,0.742089,1,0.987421,1,0.981013,1,1,0.993671,1,0.886792,1,0.984177,1,1,1,1,1,0.996835,0.968354,1,1,0.990506,0.996835,1,1,0.996835,1,1,1,1,1,0.996835,1,1,0.990506,1,1,1,0.981013,0.990506,1]
area_under_roc_curve
0.9961684479738875 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.981013,1,0.946203,1,1,0.984177,1,1,0.993671,1,0.993711,1,1,1,1,1,0.977848,1,1,0.993711,0.949686,1,1,0.996835,1,1,1,1,1,0.993671,1,0.990506,1,1,1,1,1,1,1,1,1,0.981132,0.971519,1,1,0.996835,1,1,0.996835,0.990506,1,0.993711,0.974684,0.996835,1,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.993671,0.949686,1]
area_under_roc_curve
0.9965242118461903 [0.981013,1,1,1,1,0.987342,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.955975,0.990506,1,1,1,1,0.993711,0.990506,0.977848,1,1,1,1,1,1,0.990506,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993711,0.939873,1,0.987342,1,0.996835,0.993711,1,1,1,0.996835,1,0.996835,1,0.977848,0.993671,1,1,0.996835,0.996835,0.987421,1,0.993711,1,1,1,1,1,0.981132,1,1,0.993671,1,0.987421,1,1,1,1,1,1,0.993711,1,1,0.993711,1,1,1,1]
area_under_roc_curve
0.9929287576626065 [1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.880503,1,1,1,0.996835,1,0.987342,0.996835,1,1,1,1,0.971519,0.977848,1,1,1,1,0.987421,1,0.977848,1,1,0.993671,0.936709,1,1,1,1,1,1,1,1,1,1,1,1,1,0.962264,0.996835,0.987421,0.984177,0.977848,1,0.993671,1,1,1,1,0.993711,1,1,1,0.968553,0.996835,0.977848,0.987342,0.990506,1,1,1,0.89557,1,1,0.977848,1,1,1,1,0.996835,0.993671,0.993711,1,1,1,1,1,1,0.993671,1,1,0.962025,1,0.996835,1,0.987421,0.996835]
area_under_roc_curve
0.9960873437624389 [0.96519,1,1,1,1,0.996835,1,0.996835,1,1,1,1,1,1,1,1,1,0.996835,1,0.962025,1,1,0.996835,1,0.996835,1,0.981013,1,1,1,1,1,0.974684,0.996835,1,0.996835,0.987342,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,0.993671,1,1,1,1,0.993671,0.990506,1,1,0.996835,1,1,1,0.987421,1,1,0.996835,0.993711,1,0.977848,1,0.987342,0.993671,1,1,0.984177,1,1,1,1,1,1,0.993671,1,0.993671,1,1,1,1,0.993671,1,1,0.993671,0.981132,1,1,1,1,0.981132,0.968553,1]
area_under_roc_curve
0.9969245083990126 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.990506,1,1,0.990506,1,0.990506,0.880503,0.981132,0.993671,1,1,1,1,1,0.974843,1,1,0.996835,1,1,1,0.993671,1,1,1,1,1,0.996835,0.996835,1,1,0.993711,1,1,1,0.993671,0.993671,1,1,0.993671,0.993711,1,1,1,0.993671,1,1,0.981132,0.996835,0.993671,0.987342,0.993711,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.955975,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1]
area_under_roc_curve
0.9946705079213436 [1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.996835,1,0.987421,0.971519,1,1,1,1,1,1,0.981132,0.993711,1,0.962264,1,1,1,1,1,1,1,0.984177,0.993671,1,1,1,1,1,1,0.993711,0.996835,1,1,0.987421,1,0.993711,0.987342,0.993671,1,0.996835,0.949367,1,1,0.981013,0.974843,1,1,1,1,1,0.955975,0.993711,1,0.955696,0.981013,0.993711,1,1,1,0.996835,1,1,1,1,1,0.974684,0.987421,0.996835,0.996835,1,0.977848,1,0.949686,1,1,1,1,1,0.981132,1,0.996835,0.993671,1,0.987342,1]
area_under_roc_curve
0.9955810644057002 [0.987421,1,1,1,1,0.993711,0.990506,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.974843,1,1,0.886792,0.981132,1,1,1,1,1,1,1,1,1,1,0.968354,1,1,1,1,0.996835,1,1,1,1,1,0.987421,1,1,0.993671,0.996835,0.952532,0.996835,0.968553,1,1,1,0.990506,1,1,0.996835,0.996835,1,1,0.974684,1,0.996835,1,1,0.981132,0.993671,1,0.996835,0.996835,1,1,1,1,1,0.974843,1,0.993711,1,1,1,0.996835,1,1,1,1,1,0.993671,1,0.993671,0.993711,1,0.968354,1]
area_under_roc_curve
0.9957009792213993 [0.930818,1,0.981132,1,1,1,1,0.993711,1,1,1,1,1,0.984177,1,1,1,0.974843,0.993671,1,1,1,1,1,1,0.90566,0.993711,1,0.993671,1,1,1,1,1,0.996835,0.993671,0.987342,0.993671,0.996835,1,1,1,1,1,1,1,1,0.996835,1,0.993711,0.993671,0.996835,1,0.990506,1,0.981132,1,0.993711,0.996835,0.990506,1,1,0.968354,0.993671,1,1,0.993671,1,1,0.993711,0.993711,1,0.987342,1,0.993671,0.996835,1,1,1,0.996835,1,0.981132,1,0.968553,1,0.996835,1,0.987342,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1]
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
0.7157632939876041
kappa
0.7662758103359786
kappa
0.7726016581129096
kappa
0.7473351756810107
kappa
0.7599020653161157
kappa
0.7536128879412461
kappa
0.7788571654227382
kappa
0.7536128879412461
kappa
0.7346705097326964
kappa
0.6778523489932886
kb_relative_information_score
117.95822678995115
kb_relative_information_score
118.37035825846495
kb_relative_information_score
118.86192961336229
kb_relative_information_score
119.91612957446561
kb_relative_information_score
117.76064681192811
kb_relative_information_score
118.84156654312156
kb_relative_information_score
122.18696916781013
kb_relative_information_score
118.74531278061546
kb_relative_information_score
119.3849855146747
kb_relative_information_score
118.08366630590594
mean_absolute_error
0.012069374549061856
mean_absolute_error
0.012114341991341246
mean_absolute_error
0.01220709740259686
mean_absolute_error
0.012274195165944628
mean_absolute_error
0.012190073818542256
mean_absolute_error
0.012236165133477927
mean_absolute_error
0.011732573728354725
mean_absolute_error
0.012138773313491863
mean_absolute_error
0.012228634514790473
mean_absolute_error
0.012387809704184752
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
0.71875
predictive_accuracy
0.76875
predictive_accuracy
0.775
predictive_accuracy
0.75
predictive_accuracy
0.7625
predictive_accuracy
0.75625
predictive_accuracy
0.78125
predictive_accuracy
0.75625
predictive_accuracy
0.7375
predictive_accuracy
0.68125
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
0.71875 [1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0,1,0,1,0,0.5,1,1,1,0,1,0,0.5,0,1,0.5,1,0.5,1,0.5,0,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,1,0,0.5,1,1,0.5,0.5,1,0.5,0.5,1,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,1,0.5,1,0,1,0.5,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0,1]
recall
0.76875 [1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,0.5,0.5,1,0,0,0.5,1,1,1,0,1,0.5,0.5,1,0,0.5,0.5,0,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1]
recall
0.775 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,0,1,0,0,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,0.5,1,1,1,0.5,0.5,0,0.5,0,1,1,1,0,1,1,0.5,0.5,1,0,0.5,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1]
recall
0.75 [0.5,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,1,1,1,1,0,0,1,1,1,1,0,0,0.5,0,1,1,1,0.5,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,0,0,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,0,0.5,0,1,0,1,1,0.5,1,1,1,0.5,1,0.5,1,0,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1]
recall
0.7625 [0.5,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1,1,1,0.5,1,0,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0.5,1,1,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,0,0.5,0,1,1,1,1,1,1,0,1,0.5,1,0,0.5,0.5,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1]
recall
0.75625 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,0,0,0,1,1,1,1,0.5,0,1,0.5,0.5,1,1,0,1,1,0,1,1,1,1,0,1,0.5,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,0,1,1,1,0.5,0,0,1]
recall
0.78125 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,1,0.5,0,0,0.5,1,1,0.5,0,1,1,1,1,0.5,1,1,1,1,1,1,1,0,0.5,0,1,1,1,0,1,1,0,0.5,0.5,1,0.5,0,0,1,1,1,0.5,1,1,0,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0,1,1,0,1,1,0,1]
recall
0.75625 [1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,0.5,0,1,1,1,1,1,0,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0.5,1,0,1,1,0,1,1,0,1,0.5,0.5,0,0,1]
recall
0.7375 [0,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,1,0,1,1,1,0,1,1,0,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,0.5,1,0.5,1,1,1,0,1,1,0.5,0.5,0.5,0.5,0,1,0,0.5,0,1,1,0.5,0.5,1,1,0.5,1,0.5,1,1,0,0,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,0,0.5,0,0.5,0.5,1]
recall
0.68125 [0,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,0,1,0.5,0.5,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,0.5,1,0,0.5,1,0,1,0,1,0,1,1,0,0.5,1,1,0,0,0,0,0,1,0,1,0,1,1,1,1,0.5,1,0,1,0,1,0.5,0.5,0,1,0.5,1,1,1,0.5,0,1,1,0.5,1,1]
relative_absolute_error
0.6095643711647392
relative_absolute_error
0.6118354541081428
relative_absolute_error
0.6165200708382242
relative_absolute_error
0.6199088467648791
relative_absolute_error
0.6156602938657695
relative_absolute_error
0.6179881380544398
relative_absolute_error
0.5925542287047831
relative_absolute_error
0.6130693592672648
relative_absolute_error
0.6176078037772956
relative_absolute_error
0.6256469547568045
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
0.06998750188993073
root_mean_squared_error
0.06936241186106187
root_mean_squared_error
0.06958574090579615
root_mean_squared_error
0.07019200724118525
root_mean_squared_error
0.0700148576004411
root_mean_squared_error
0.07019932618406323
root_mean_squared_error
0.06778728039847369
root_mean_squared_error
0.06959758703810619
root_mean_squared_error
0.0697488306470419
root_mean_squared_error
0.07150605646430308
root_relative_squared_error
0.7034008599490592
root_relative_squared_error
0.6971184687795124
root_relative_squared_error
0.699363010131549
root_relative_squared_error
0.7054562160633958
root_relative_squared_error
0.7036757951843212
root_relative_squared_error
0.7055297742069966
root_relative_squared_error
0.6812878019404556
root_relative_squared_error
0.6994820682409106
root_relative_squared_error
0.7010021237038773
root_relative_squared_error
0.7186629076668498
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
2470.4410769991227
usercpu_time_millis
2387.63397799994
usercpu_time_millis
2464.4572339984734
usercpu_time_millis
2400.2272130019264
usercpu_time_millis
2428.788061999512
usercpu_time_millis
2413.1908299987117
usercpu_time_millis
2374.563322002359
usercpu_time_millis
2375.1179020018753
usercpu_time_millis
2377.3564229995827
usercpu_time_millis
2383.1572530016274
usercpu_time_millis_testing
39.078853000319214
usercpu_time_millis_testing
36.33918800005631
usercpu_time_millis_testing
43.5171339995577
usercpu_time_millis_testing
38.135344000693294
usercpu_time_millis_testing
37.78427599900169
usercpu_time_millis_testing
36.91324099963822
usercpu_time_millis_testing
36.9655030008289
usercpu_time_millis_testing
37.046363000627025
usercpu_time_millis_testing
37.17445000074804
usercpu_time_millis_testing
37.75459800090175
usercpu_time_millis_training
2431.3622239988035
usercpu_time_millis_training
2351.2947899998835
usercpu_time_millis_training
2420.9400999989157
usercpu_time_millis_training
2362.091869001233
usercpu_time_millis_training
2391.0037860005104
usercpu_time_millis_training
2376.2775889990735
usercpu_time_millis_training
2337.5978190015303
usercpu_time_millis_training
2338.0715390012483
usercpu_time_millis_training
2340.1819729988347
usercpu_time_millis_training
2345.4026550007256