5983952
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)
4018685
bootstrap
false
6902
class_weight
null
6902
criterion
"entropy"
6902
max_depth
null
6902
max_features
0.28363242048443604
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
6
6902
min_samples_split
16
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
56872
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
"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
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
12891217
description
https://api.openml.org/data/download/12891217/description.xml
-1
12891218
predictions
https://api.openml.org/data/download/12891218/predictions.arff
area_under_roc_curve
0.995707267992424 [0.990175,0.999724,1,1,0.999408,0.994081,0.999527,0.999566,0.997475,0.999842,0.998185,1,0.998816,0.988439,0.999842,0.999092,0.999842,0.985874,0.988518,0.97021,0.998816,1,0.993687,1,0.99854,0.972143,0.990372,0.998382,0.998185,0.999882,0.999921,1,0.999408,0.989978,0.999487,0.999014,0.991162,0.992266,0.998974,0.999882,0.999408,0.998974,0.999961,0.999132,0.999211,0.999566,0.999487,0.998501,0.99779,0.997396,0.99783,0.993174,0.998382,0.983112,0.996765,0.983152,1,0.999882,0.996173,0.987334,1,0.99929,0.99345,0.999171,0.99858,0.995699,0.979759,1,0.996686,0.989504,0.994871,0.998106,0.993884,0.999527,0.975024,0.998974,0.999605,0.989623,1,0.999882,0.994279,0.996922,0.996409,0.995857,0.997869,0.994831,0.999961,0.993371,0.998224,1,0.999803,0.999605,0.998935,0.993371,0.999763,0.999014,0.997948,0.996133,0.96368,0.999171]
average_cost
0
f_measure
0.7853234899325297 [0.666667,0.888889,1,1,0.969697,0.689655,0.9375,0.857143,0.83871,0.967742,0.714286,0.903226,0.823529,0.538462,0.969697,0.896552,0.941176,0.533333,0.588235,0.222222,0.810811,0.967742,0.774194,1,0.864865,0.380952,0.352941,0.833333,0.875,0.914286,0.909091,1,0.848485,0.571429,0.909091,0.833333,0.5,0.536585,0.909091,0.969697,0.8,0.903226,0.967742,0.864865,0.823529,0.882353,0.83871,0.827586,0.764706,0.903226,0.8,0.666667,0.823529,0.48,0.689655,0.181818,1,0.969697,0.611111,0.642857,0.941176,0.756757,0.516129,0.823529,0.857143,0.774194,0.571429,0.914286,0.642857,0.592593,0.689655,0.709677,0.666667,0.941176,0.758621,0.967742,0.914286,0.666667,0.969697,0.903226,0.733333,0.666667,0.733333,0.709677,0.727273,0.875,0.969697,0.666667,0.875,0.969697,0.888889,0.789474,0.8,0.72,0.914286,0.823529,0.848485,0.777778,0.434783,0.882353]
kappa
0.7916666666666666
kb_relative_information_score
1150.9115099584794
mean_absolute_error
0.01339259087475
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
0.7966338137420075 [0.818182,0.8,1,1,0.941176,0.769231,0.9375,0.789474,0.866667,1,0.833333,0.933333,0.777778,0.7,0.941176,1,0.888889,0.571429,0.555556,0.272727,0.714286,1,0.8,1,0.761905,0.8,0.333333,0.75,0.875,0.842105,0.882353,1,0.823529,0.526316,0.882353,0.75,0.5,0.44,0.882353,0.941176,0.736842,0.933333,1,0.761905,0.777778,0.833333,0.866667,0.923077,0.722222,0.933333,0.857143,0.714286,0.777778,0.666667,0.769231,0.333333,1,0.941176,0.55,0.75,0.888889,0.666667,0.533333,0.777778,0.789474,0.8,0.666667,0.842105,0.75,0.727273,0.769231,0.733333,0.565217,0.888889,0.846154,1,0.842105,1,0.941176,0.933333,0.785714,0.714286,0.785714,0.733333,0.705882,0.875,0.941176,0.565217,0.875,0.941176,0.8,0.681818,0.857143,1,0.842105,0.777778,0.823529,0.7,0.714286,0.833333]
predictive_accuracy
0.79375
prior_entropy
6.6438561897747395
recall
0.79375 [0.5625,1,1,1,1,0.625,0.9375,0.9375,0.8125,0.9375,0.625,0.875,0.875,0.4375,1,0.8125,1,0.5,0.625,0.1875,0.9375,0.9375,0.75,1,1,0.25,0.375,0.9375,0.875,1,0.9375,1,0.875,0.625,0.9375,0.9375,0.5,0.6875,0.9375,1,0.875,0.875,0.9375,1,0.875,0.9375,0.8125,0.75,0.8125,0.875,0.75,0.625,0.875,0.375,0.625,0.125,1,1,0.6875,0.5625,1,0.875,0.5,0.875,0.9375,0.75,0.5,1,0.5625,0.5,0.625,0.6875,0.8125,1,0.6875,0.9375,1,0.5,1,0.875,0.6875,0.625,0.6875,0.6875,0.75,0.875,1,0.8125,0.875,1,1,0.9375,0.75,0.5625,1,0.875,0.875,0.875,0.3125,0.9375]
relative_absolute_error
0.6763934785227259
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.0728943996391737
root_relative_squared_error
0.7326162815798696
total_cost
0
area_under_roc_curve
0.9945038213518032 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.974684,0.974684,0.993711,1,1,0.993711,1,1,0.993671,0.993671,1,0.993671,1,0.996835,1,0.996835,0.993671,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.981013,0.993671,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.974843,0.990506,1,0.993671,1,1,1,1,0.987342,1,1,1,1,0.993711,1,0.993671,0.971519,1,0.996835,0.993671,1,1,1,1,0.984177,1,0.990506,1,1,0.810127,1]
area_under_roc_curve
0.9956129090040603 [1,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,0.952532,1,1,1,0.984177,0.996835,0.987421,1,1,1,1,1,0.955696,0.990506,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,0.952532,1,1,1,0.981132,1,1,1,0.936709,1,1,1,0.993671,1,1,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.987342,1,1,0.990506,0.996835,0.968553,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,1,0.990506,0.996835,1]
area_under_roc_curve
0.9954559250855823 [0.996835,1,1,1,1,0.990506,1,1,0.987342,1,0.996835,1,1,1,0.996835,1,1,0.993671,0.968553,0.93038,1,1,0.958861,1,1,0.981013,1,1,1,1,1,1,1,0.974684,1,1,0.993711,0.962264,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.993711,0.984177,1,1,1,1,1,0.984177,0.993671,1,0.993711,0.987342,1,1,0.993711,1,1,1,1,0.974684,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,1,0.987342,0.968553,1]
area_under_roc_curve
0.9955736008279598 [1,0.996835,1,1,0.996835,0.981013,1,1,0.993671,1,1,1,1,1,1,1,1,0.996835,0.943396,0.990506,1,1,1,1,0.987421,0.96519,0.987342,1,1,1,1,1,1,0.981013,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.946203,1,0.990506,1,1,1,0.993671,1,1,0.993671,1,0.996835,1,0.96519,1,1,0.968354,0.990506,0.996835,0.981132,1,1,1,1,0.993671,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,0.993671,1,1,1,0.996835,1,0.990506]
area_under_roc_curve
0.9941557698431656 [0.993671,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,0.949686,1,1,1,0.993671,1,0.987342,0.987342,1,1,1,1,0.968354,0.987342,1,1,1,1,1,1,0.984177,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.996835,0.993711,0.977848,0.971519,1,0.996835,1,0.993711,1,1,0.993711,1,1,1,0.861635,1,0.990506,0.987342,1,1,0.993711,1,0.889241,1,1,0.990506,1,1,1,1,0.996835,0.993671,0.993711,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.996835,1,0.987421,0.996835]
area_under_roc_curve
0.9956104211448131 [0.958861,1,1,1,1,0.993671,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.920886,1,1,0.990506,1,0.990506,0.993671,0.981013,1,1,1,1,1,0.987342,0.993671,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1,1,1,1,0.993671,0.958861,1,1,0.993671,1,1,1,0.987421,1,1,1,1,1,1,1,0.981013,0.996835,1,1,0.977848,1,1,0.990506,1,1,1,0.996835,1,0.996835,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987421,1,1]
area_under_roc_curve
0.9968040960114639 [1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.987421,0.993671,0.971519,0.996835,1,1,1,0.993671,0.924528,0.993711,0.987342,1,1,1,1,1,0.993711,1,1,0.993671,1,1,1,1,0.996835,1,1,1,0.996835,1,0.993671,1,1,1,1,1,1,0.993671,0.996835,1,1,1,0.993711,1,1,1,0.987342,1,1,0.968553,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.987342,1,0.993671,1,1,1,1,0.937107,1,1,1,1,1,0.993711,1,1,1,1,0.974684,1]
area_under_roc_curve
0.9966841811957646 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.990506,1,0.987421,0.987342,0.987342,1,1,1,1,1,0.974843,0.987421,0.996835,0.987421,0.996835,1,1,1,1,1,1,0.987342,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.990506,1,1,0.984177,1,1,0.996835,1,1,0.996835,1,1,1,0.949686,0.968553,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,1,1,1,0.977848,0.993711,0.993671,0.996835,1,0.987342,1,1,0.993711,1,1,1,1,0.987421,1,1,0.996835,1,0.974684,1]
area_under_roc_curve
0.9973947137966721 [0.993711,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.993671,0.981013,1,1,0.981132,0.996835,1,0.993671,1,1,1,1,0.993711,1,0.996835,0.996835,1,0.958861,1,0.955975,1,1,0.996835,0.993671,1,1,0.996835,1,1,1,1,1,0.996835,0.993711,1,0.987421,0.987342,1,1,0.996835,1,1,1,1,0.990506,1,1,0.993711,0.996835,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,1,0.996835,1]
area_under_roc_curve
0.9978308355226494 [0.949686,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,0.987421,0.993671,0.993711,1,1,1,1,1,0.962264,1,1,0.996835,1,1,1,1,0.987421,1,1,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.984177,1,1,1,1,0.993671,1,1,1,0.974684,1,0.993711,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,0.974843,1,0.993711,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1]
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
0.7662850375049349
kappa
0.8357678641926569
kappa
0.7789444597955236
kappa
0.7789095503178175
kappa
0.7852094602598018
kappa
0.7662296635602591
kappa
0.7914938988271532
kappa
0.8041461006910168
kappa
0.8041538340045803
kappa
0.8041461006910168
kb_relative_information_score
114.15746882067033
kb_relative_information_score
114.48351453934546
kb_relative_information_score
114.77822989578479
kb_relative_information_score
114.34684177716802
kb_relative_information_score
114.45713954605972
kb_relative_information_score
114.07285790416985
kb_relative_information_score
116.83032428126143
kb_relative_information_score
115.31719118841639
kb_relative_information_score
115.87242635547292
kb_relative_information_score
116.5955156501313
mean_absolute_error
0.013411171262765156
mean_absolute_error
0.013489432224025748
mean_absolute_error
0.013391811865218022
mean_absolute_error
0.013514980158729265
mean_absolute_error
0.01337958657314938
mean_absolute_error
0.013579318358724427
mean_absolute_error
0.013058036023004472
mean_absolute_error
0.013308893093018356
mean_absolute_error
0.013444416468947062
mean_absolute_error
0.01334826271991838
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
0.76875
predictive_accuracy
0.8375
predictive_accuracy
0.78125
predictive_accuracy
0.78125
predictive_accuracy
0.7875
predictive_accuracy
0.76875
predictive_accuracy
0.79375
predictive_accuracy
0.80625
predictive_accuracy
0.80625
predictive_accuracy
0.80625
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
0.76875 [1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,0.5,0,1,0,0,1,1,0.5,0,1,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,1,0,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,1,0,1]
recall
0.8375 [1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,1,0,0.5,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,0.5,1,1,0.5,1,1,0,0,1,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1]
recall
0.78125 [0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,0,0.5,0.5,1,0,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0.5,0,1]
recall
0.78125 [0.5,1,1,1,1,0,1,1,0.5,1,0,0.5,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,0,1,0,0.5,0,0,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0.5]
recall
0.7875 [0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,0,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,0,0,0.5,0,0,0,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1]
recall
0.76875 [0.5,1,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0.5,0.5,0,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0,1,0.5,1,1,1,1,0.5,0,1,1,0,1,1,0.5,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,0,1,1,1,1,0,0,1]
recall
0.79375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,0,1,0.5,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0.5,1,0,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,0,1,1,1,0,0,1,1,1,1,0.5,1]
recall
0.80625 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1,1,0.5,0.5,1,1,0.5,1,1,0,0,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,0,0,0,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0,1]
recall
0.80625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,0.5,1,1,1,0,1,0.5,0.5,1,0.5,0.5,0,1,1,0.5,0,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0,0.5,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1]
recall
0.80625 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,0,0.5,0,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,0,1,0,1,1,0,0.5,1,0,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1]
relative_absolute_error
0.677331881957835
relative_absolute_error
0.681284455758875
relative_absolute_error
0.6763541346069697
relative_absolute_error
0.6825747554913759
relative_absolute_error
0.6757366956136039
relative_absolute_error
0.6858241595315355
relative_absolute_error
0.6594967688386086
relative_absolute_error
0.6721663178292087
relative_absolute_error
0.6790109327751029
relative_absolute_error
0.6741546828241595
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
0.07291080377097754
root_mean_squared_error
0.07315523019501032
root_mean_squared_error
0.07302780533517689
root_mean_squared_error
0.07342918370645563
root_mean_squared_error
0.07293112388943769
root_mean_squared_error
0.0739027743274998
root_mean_squared_error
0.07159623216388503
root_mean_squared_error
0.07268605188032762
root_mean_squared_error
0.07277909321172958
root_mean_squared_error
0.07250316346517408
root_relative_squared_error
0.7327811493077634
root_relative_squared_error
0.7352377272997787
root_relative_squared_error
0.7339570592724116
root_relative_squared_error
0.7379910636860345
root_relative_squared_error
0.7329853741823935
root_relative_squared_error
0.7427508285170493
root_relative_squared_error
0.7195692075478228
root_relative_squared_error
0.7305223078162217
root_relative_squared_error
0.731457408380632
root_relative_squared_error
0.7286842100842003
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
4881.406814999991
usercpu_time_millis
4874.357651000011
usercpu_time_millis
4860.535367000011
usercpu_time_millis
4860.090260000007
usercpu_time_millis
4856.854944999995
usercpu_time_millis
4857.556205999998
usercpu_time_millis
4868.506232000002
usercpu_time_millis
4871.349344999984
usercpu_time_millis
4868.706585000012
usercpu_time_millis
4857.607068999997
usercpu_time_millis_testing
52.670762999994736
usercpu_time_millis_testing
39.29989899999953
usercpu_time_millis_testing
39.31789800000729
usercpu_time_millis_testing
39.15951300000131
usercpu_time_millis_testing
39.28375599998901
usercpu_time_millis_testing
39.01183199999991
usercpu_time_millis_testing
38.89288099999533
usercpu_time_millis_testing
39.15458099999114
usercpu_time_millis_testing
39.09825300000591
usercpu_time_millis_testing
39.06382899999983
usercpu_time_millis_training
4828.736051999997
usercpu_time_millis_training
4835.0577520000115
usercpu_time_millis_training
4821.217469000004
usercpu_time_millis_training
4820.930747000006
usercpu_time_millis_training
4817.571189000006
usercpu_time_millis_training
4818.544373999998
usercpu_time_millis_training
4829.613351000006
usercpu_time_millis_training
4832.1947639999935
usercpu_time_millis_training
4829.608332000006
usercpu_time_millis_training
4818.543239999997