5979142
1
Jan van Rijn
9956
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)
4013875
bootstrap
true
6902
class_weight
null
6902
criterion
"gini"
6902
max_depth
null
6902
max_features
0.7734569738157152
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
7
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
47891
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
1493
one-hundred-plants-texture
https://www.openml.org/data/download/1592285/phpoOxxNn
-1
12881621
description
https://api.openml.org/data/download/12881621/description.xml
-1
12881622
predictions
https://api.openml.org/data/download/12881622/predictions.arff
area_under_roc_curve
0.992417505087374 [0.996002,0.999882,0.982746,0.994157,0.962768,0.993564,0.991314,0.99463,0.999013,0.998973,0.995657,0.984089,0.999289,0.996012,0.97635,0.999013,0.985629,0.995696,0.999684,0.972007,0.999763,0.97564,0.997197,0.998697,0.952029,0.995183,0.997315,0.959531,0.999882,0.999605,0.992735,1,0.957833,0.998737,0.996012,0.988155,0.992577,0.999684,0.98784,0.995302,0.999447,0.993683,0.98855,0.96802,0.988195,0.998776,0.987958,0.999566,0.981917,0.998697,0.996486,0.998816,0.998105,0.997552,0.986734,0.996447,1,0.989261,0.99921,0.998737,0.999803,0.997868,0.999447,0.997434,0.996368,0.99775,0.999526,0.992143,0.999013,0.9756,0.998776,0.981917,1,0.997276,0.999724,0.999171,0.991314,0.998105,0.999605,0.992735,0.99692,0.989182,0.979232,0.995696,0.983852,0.99775,0.966875,0.998934,0.99925,0.998855,0.991393,0.97868,0.999921,0.998895,0.995539,0.99542,0.999447,0.998579,0.998658,0.998973]
average_cost
0
f_measure
0.7297870388216392 [0.785714,1,0.666667,0.666667,0.611111,0.6875,0.583333,0.774194,0.833333,0.8,0.875,0.642857,0.909091,0.875,0.608696,0.857143,0.342857,0.857143,0.909091,0.823529,0.941176,0.454545,0.75,0.882353,0.285714,0.588235,0.8,0.2,0.941176,0.8,0.583333,0.941176,0.64,0.736842,0.733333,0.210526,0.717949,0.909091,0.592593,0.705882,0.857143,0.5,0.466667,0.5,0.580645,0.756757,0.578947,0.967742,0.6875,0.769231,0.736842,0.709677,0.764706,0.756757,0.645161,0.615385,0.941176,0.75,0.882353,0.875,0.882353,0.787879,0.833333,0.764706,0.83871,0.740741,0.882353,0.685714,0.777778,0.56,0.848485,0.56,0.888889,0.75,0.896552,0.8,0.75,0.823529,0.882353,0.571429,0.8,0.714286,0.62069,0.545455,0.6,0.8,0.56,0.789474,0.909091,0.780488,0.933333,0.1,0.941176,0.833333,0.736842,0.666667,0.8,0.810811,0.83871,0.8125]
kappa
0.7422628524472292
kb_relative_information_score
1111.7342139642021
mean_absolute_error
0.013574267000158393
mean_prior_absolute_error
0.019799992711748222
number_of_instances
1599 [15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16]
precision
0.7512767675978983 [0.846154,1,1,0.818182,0.55,0.6875,0.875,0.8,0.75,0.857143,0.875,0.75,0.882353,0.875,1,0.789474,0.315789,0.789474,0.882353,0.777778,0.888889,0.833333,0.75,0.833333,0.6,0.555556,0.857143,0.5,0.888889,0.666667,0.875,0.888889,0.888889,0.636364,0.785714,0.666667,0.608696,0.882353,0.727273,0.666667,0.789474,0.583333,0.5,0.75,0.6,0.666667,0.5,1,0.6875,0.652174,0.636364,0.733333,0.722222,0.666667,0.666667,0.521739,0.888889,0.75,0.833333,0.875,0.833333,0.764706,0.75,0.722222,0.866667,0.909091,0.833333,0.631579,0.7,0.777778,0.823529,0.777778,0.8,0.625,1,0.736842,0.75,0.777778,0.833333,0.666667,0.736842,0.833333,0.692308,0.529412,0.642857,0.736842,0.777778,0.681818,0.882353,0.64,1,0.25,0.888889,0.75,0.636364,0.6,0.736842,0.714286,0.866667,0.8125]
predictive_accuracy
0.7448405253283302
prior_entropy
6.6438309601245775
recall
0.7448405253283302 [0.733333,1,0.5,0.5625,0.6875,0.6875,0.4375,0.75,0.9375,0.75,0.875,0.5625,0.9375,0.875,0.4375,0.9375,0.375,0.9375,0.9375,0.875,1,0.3125,0.75,0.9375,0.1875,0.625,0.75,0.125,1,1,0.4375,1,0.5,0.875,0.6875,0.125,0.875,0.9375,0.5,0.75,0.9375,0.4375,0.4375,0.375,0.5625,0.875,0.6875,0.9375,0.6875,0.9375,0.875,0.6875,0.8125,0.875,0.625,0.75,1,0.75,0.9375,0.875,0.9375,0.8125,0.9375,0.8125,0.8125,0.625,0.9375,0.75,0.875,0.4375,0.875,0.4375,1,0.9375,0.8125,0.875,0.75,0.875,0.9375,0.5,0.875,0.625,0.5625,0.5625,0.5625,0.875,0.4375,0.9375,0.9375,1,0.875,0.0625,1,0.9375,0.875,0.75,0.875,0.9375,0.8125,0.8125]
relative_absolute_error
0.6855692927656571
root_mean_prior_squared_error
0.09949872432040595
root_mean_squared_error
0.07393833925926008
root_relative_squared_error
0.7431084143467379
total_cost
0
area_under_roc_curve
0.9952969508797069 [1,1,0.974843,1,0.996835,0.968354,0.990506,1,1,1,1,0.990506,1,1,0.968354,1,0.993671,1,1,1,1,0.962025,1,0.993671,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.971519,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,0.987342,1,1,1,1,1,0.924528,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.943038,1,1,0.993671,0.987342,0.971519,1,1,0.981013,0.996835,1,1,1,1]
area_under_roc_curve
0.9896870770639277 [1,1,1,0.996835,0.990506,0.993671,1,1,1,0.996835,1,1,1,1,1,1,0.974684,1,1,0.93038,1,0.924051,0.996835,1,0.917722,0.981013,0.949686,1,1,1,0.984177,1,1,1,0.974843,0.990506,1,1,0.990506,1,1,0.993711,0.962264,0.81962,1,1,0.946203,1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,0.993671,1,0.981132,1,0.996835,1,0.924528,1,0.946203,1,0.996835,1,1,1,0.996835,1,0.993671,1,0.993711,1,0.996835,0.955975,1,1,1,0.993671,0.993671,1,1,1,1,1,0.981132,1,0.981013,1,1,1,1]
area_under_roc_curve
0.995136981530133 [1,1,0.993671,1,1,1,1,1,1,1,1,0.993711,1,0.984177,1,1,0.996835,0.996835,1,1,1,0.981013,0.996835,1,0.981132,0.996835,1,0.977848,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,0.993711,0.993711,1,0.981013,1,0.987342,1,1,1,1,0.990506,1,1,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.955696,1,1,1,1,1,1,1,0.990506,1,0.955696,1,0.990506,0.993711,0.990506,0.905063,1,1,1,1,0.977848,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.990161511822307 [1,1,0.990506,1,1,0.993711,0.958861,0.990506,1,1,1,1,1,0.987342,0.844937,1,0.987342,1,1,1,1,1,1,1,0.937107,0.996835,1,0.85443,1,1,1,1,0.971519,1,0.996835,0.987421,1,1,0.874214,1,1,0.993711,0.962264,0.996835,0.968354,0.996835,1,1,1,1,1,1,1,0.990506,0.987342,0.993671,1,1,1,1,1,1,1,0.981132,1,1,1,1,1,0.993671,1,0.939873,1,1,1,1,0.933544,0.993711,1,1,1,0.996835,1,0.996835,1,1,1,1,0.993671,1,1,0.987342,1,1,1,1,1,1,1,0.993711]
area_under_roc_curve
0.994593135498766 [1,1,0.977848,1,0.987421,1,1,0.971519,1,1,1,0.993711,0.993711,1,0.981132,1,0.977848,1,1,1,1,0.874214,1,1,0.993711,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.955975,1,0.996835,0.987342,0.990506,0.993671,1,1,1,1,1,0.990506,0.996835,1,1,0.955696,0.996835,1,0.996835,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,0.996835,1,0.993711,1,1,0.993671,0.993671,0.977848,0.968553,1,1,1,0.955696,1,1,0.993711,0.962264,0.990506,1,0.993711,1,1,1,1,0.996835,1]
area_under_roc_curve
0.9960903291935355 [1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,0.993711,1,1,0.899371,1,1,0.987421,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,1,0.946203,1,0.993671,1,1,1,0.993671,1,1,1,0.993671,0.993671,1,0.955696,1,1,1,0.981132,0.996835,1,1,1,1,0.990506,0.996835,0.996835,1,1,1,1,1,1,1,1,1,0.996835,0.990506,0.993711,0.987342,0.993711,0.949367,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.9928133209935514 [0.993711,0.996835,1,0.974843,1,0.990506,1,1,1,1,0.974684,0.905063,1,1,1,1,1,1,1,1,1,1,0.981132,1,0.946203,1,1,1,1,1,0.993711,1,0.993671,1,0.996835,0.977848,0.990506,1,1,1,1,1,0.987342,1,0.996835,0.996835,0.993711,1,1,1,1,1,0.993671,1,1,0.987421,1,0.987342,1,1,0.996835,1,1,0.993671,0.974843,1,1,1,0.990506,0.949686,1,1,1,0.974684,1,1,1,0.993671,1,1,1,1,0.908228,0.987421,1,1,1,0.993671,1,1,1,0.849057,1,1,0.993711,1,1,1,1,1]
area_under_roc_curve
0.9885822187723903 [0.930818,1,0.924051,1,1,1,1,1,0.996835,1,0.987342,1,1,1,1,1,0.981132,0.974684,1,0.901899,1,1,1,1,0.924051,0.993711,0.996835,0.949686,1,1,0.974843,1,0.718354,1,1,0.993671,1,0.990506,0.993671,1,1,0.993671,1,0.981132,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.968553,1,1,1,0.993711,1,0.990506,0.996835,0.96519,1,1,1,0.987421,0.939873,0.993711,0.90566,1,1,1,1,0.987421,1,1,1,0.984177,1,1,1,1]
area_under_roc_curve
0.9939184280710132 [0.974843,1,1,0.977848,0.993671,1,1,0.993671,0.990506,1,1,1,1,1,1,0.993671,0.993711,1,1,1,1,0.990506,0.993711,1,0.946203,1,1,0.981013,1,1,0.996835,1,1,1,0.987421,1,0.955696,1,1,1,0.981132,1,0.996835,0.987421,0.993711,0.993711,1,0.993711,0.882911,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,0.996835,0.987342,1,1,1,1,0.867925,0.993671,0.990506,1,1,1,1,0.993671,1,1,0.981132,0.996835,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.993671]
area_under_roc_curve
0.9939951634748544 [1,1,1,0.984076,0.780255,1,0.984076,1,1,1,1,1,1,1,1,1,0.974684,1,1,1,1,0.987261,1,1,0.923567,1,1,0.993631,1,1,1,1,1,0.987342,1,1,1,1,0.996815,0.996815,1,0.984076,0.984076,0.987342,1,1,0.962025,1,0.987261,1,0.993631,1,1,1,1,1,1,1,1,0.996815,1,1,1,1,1,0.996815,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993631,1,0.993631,1,1,1,1,1,1,1,0.993671,1,1,0.996815,1,1,1,0.993671,1]
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
0.7536906923502013
kappa
0.6589967241583455
kappa
0.7410287789664838
kappa
0.7347019344650613
kappa
0.7283960364770439
kappa
0.7788658979624072
kappa
0.7725387987205308
kappa
0.7409981048641819
kappa
0.7284281992579142
kappa
0.783863745402207
kb_relative_information_score
111.56298281222404
kb_relative_information_score
108.29024290771922
kb_relative_information_score
112.52564507331083
kb_relative_information_score
108.97322233875339
kb_relative_information_score
110.83499908006162
kb_relative_information_score
115.49467966301444
kb_relative_information_score
111.75427220826342
kb_relative_information_score
109.74217057900835
kb_relative_information_score
110.01161277631438
kb_relative_information_score
112.54438652553252
mean_absolute_error
0.01355189894191669
mean_absolute_error
0.013839312461032557
mean_absolute_error
0.013582838167570836
mean_absolute_error
0.01376281612119217
mean_absolute_error
0.013560461475719713
mean_absolute_error
0.013000821554114177
mean_absolute_error
0.013336438596404048
mean_absolute_error
0.013785018915549238
mean_absolute_error
0.013921323169365087
mean_absolute_error
0.013400655527012258
mean_prior_absolute_error
0.01980002942907592
mean_prior_absolute_error
0.01980002942907592
mean_prior_absolute_error
0.019800029429075917
mean_prior_absolute_error
0.019800029429075917
mean_prior_absolute_error
0.01980002942907591
mean_prior_absolute_error
0.01979995585638609
mean_prior_absolute_error
0.01979995585638609
mean_prior_absolute_error
0.01979995585638609
mean_prior_absolute_error
0.019799955856386102
mean_prior_absolute_error
0.01979995631910742
number_of_instances
160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,1,2,2,2,2,1,1,2,2,1,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,2,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1]
number_of_instances
160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,2,2,2,2,2,2,1,2,2,2,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,1,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,1,2,1,2,2,2,2,2,2,2,1,1,2,2,1,1,2,1]
number_of_instances
160 [2,1,2,2,2,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,1,1,2,2,2,1,2,1,1,1,1,2,1,1,1,2,2,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,2,1,1,2,2,1]
number_of_instances
160 [2,1,2,2,2,1,2,2,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,1,1,1,2,2,2,2,2,1,1,1,1,2,1,1,1,2,1,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,1,1,2,2,2,1]
number_of_instances
160 [2,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,2,2,1,2,2,1,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,2,1,1,2,2,2,1,2,1,1,2,2,1,1,1,2,2,2,2]
number_of_instances
160 [1,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,1,2,1,2,2,2,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,1,2,1,2,2,2,1,2,1,1,2,2,2,1,1,2,2,2,2]
number_of_instances
160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,1,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,1,2,2,2,1,2]
number_of_instances
160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,1,1,1,2,2,1,2,1,1,1,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,2,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,2,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,2,2,2,2,1,2]
number_of_instances
160 [1,2,1,2,2,2,2,2,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,1,1,2,2,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,1,2,2,2,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2]
number_of_instances
159 [1,2,1,2,2,2,2,1,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,2,1,2,1,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,2,1,1,1,2]
predictive_accuracy
0.75625
predictive_accuracy
0.6625
predictive_accuracy
0.74375
predictive_accuracy
0.7375
predictive_accuracy
0.73125
predictive_accuracy
0.78125
predictive_accuracy
0.775
predictive_accuracy
0.74375
predictive_accuracy
0.73125
predictive_accuracy
0.7861635220125787
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
recall
0.75625 [1,1,0,1,0.5,0.5,0,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0.5,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0,0.5,1,1,1,0,1,1,0.5,1,0.5,1,1,1,0,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,0,1,1,0.5,0.5,1,1,1,1]
recall
0.6625 [1,1,1,0,0.5,0,0.5,1,1,0,1,1,1,1,0,1,0.5,1,1,0.5,1,0.5,1,1,0.5,0,0,0,1,1,0,1,1,1,0,0,1,1,0,0.5,1,0,0,0.5,1,1,0,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,1,0,0.5,0,1,0.5,1,0.5,1,1,1,0.5,0.5,1,1,1,1,0.5,0,0.5,1,1,0,1,1,1,1,0,1,1,1,0.5,0,1,0,1]
recall
0.74375 [1,1,0.5,0.5,1,1,1,1,1,1,1,0,1,0.5,0.5,1,0.5,1,1,1,1,0,0.5,1,0,0,1,0,1,1,0,1,0,0.5,1,1,1,1,1,1,1,1,0,0,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0,1,0,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,0,1,0.5,0,1,1,1,1,0,1,1,1,1,1,1,1,1]
recall
0.7375 [0.5,1,0.5,1,1,0,0,0,1,1,1,0,1,0.5,0,1,0.5,1,0.5,1,1,0.5,1,1,0,0.5,1,0,1,1,0.5,1,0.5,1,0.5,0,1,1,0,1,1,0,0,1,0.5,0.5,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,0,1,1,0.5,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1]
recall
0.73125 [0.5,1,0.5,1,1,1,1,0.5,1,0,1,1,0,1,0,1,0,1,1,1,1,0,0.5,1,0,0.5,1,1,1,1,1,1,0.5,1,0.5,0,1,1,1,0,1,1,0.5,0,0.5,0.5,1,1,1,1,0.5,0.5,1,0,0,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,0,1,0,0,1,0.5,1,0.5,1,1,1,0,0.5,1,0,1,1,0.5,1,1,1]
recall
0.78125 [1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0,0,1,1,0,1,1,1,0.5,0,1,1,1,1,1,0,0.5,1,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,1,0.5,0.5,0.5,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0,0,1,0,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,0.5]
recall
0.775 [1,1,0,0,1,0.5,0,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0,1,0,1,1,0,1,1,0,1,0.5,1,0.5,0,0.5,1,1,1,1,0.5,0.5,1,0,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,0.5,0,1,1,1,0.5,1,1,1,0.5,1,1,1,1,0,1,0,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1]
recall
0.74375 [0,1,0.5,1,1,1,1,1,0.5,0,0.5,0.5,1,1,1,1,0,0.5,1,0.5,1,0,1,1,0,1,0.5,0,1,1,0,1,0,0.5,1,0,1,0.5,0,1,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,0,1,0.5,1,0,1,1,1,0,0.5,0,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1]
recall
0.73125 [0,1,1,0,0.5,1,0,1,1,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,0.5,0.5,1,0.5,1,0,0.5,0,0,0,1,1,1,0,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0,1,0]
recall
0.7861635220125787 [1,1,1,0.5,0,1,0.5,0,1,1,1,0.5,1,1,0.5,1,0,1,1,1,1,0,0,1,0.5,1,1,0,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,0,1,1,0,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,0.5,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,1,0,1]
relative_absolute_error
0.6844383232085511
relative_absolute_error
0.6989541359322337
relative_absolute_error
0.6860009080403047
relative_absolute_error
0.6950906901674485
relative_absolute_error
0.6848707737679659
relative_absolute_error
0.6566086130904688
relative_absolute_error
0.6735590065521605
relative_absolute_error
0.6962146287363135
relative_absolute_error
0.7030986973071977
relative_absolute_error
0.6768022772898904
root_mean_prior_squared_error
0.09949890883178135
root_mean_prior_squared_error
0.09949890883178135
root_mean_prior_squared_error
0.09949890883178135
root_mean_prior_squared_error
0.09949890883178135
root_mean_prior_squared_error
0.09949890883178135
root_mean_prior_squared_error
0.09949853911503082
root_mean_prior_squared_error
0.09949853911503082
root_mean_prior_squared_error
0.09949853911503082
root_mean_prior_squared_error
0.09949853911503082
root_mean_prior_squared_error
0.0994985414402977
root_mean_squared_error
0.07347602308959622
root_mean_squared_error
0.07581171796161507
root_mean_squared_error
0.0738853045193491
root_mean_squared_error
0.07509492803154362
root_mean_squared_error
0.07423766715413144
root_mean_squared_error
0.07141113219709044
root_mean_squared_error
0.07292656734221831
root_mean_squared_error
0.07445782154370838
root_mean_squared_error
0.07533574528482616
root_mean_squared_error
0.07262673548391074
root_relative_squared_error
0.7384605917017549
root_relative_squared_error
0.7619351694578558
root_relative_squared_error
0.7425740180152518
root_relative_squared_error
0.7547311715599162
root_relative_squared_error
0.7461153898646462
root_relative_squared_error
0.7177103586870922
root_relative_squared_error
0.7329410862797442
root_relative_squared_error
0.7483308016977743
root_relative_squared_error
0.7571542854285537
root_relative_squared_error
0.7299276394668469
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
5597.289563000004
usercpu_time_millis
5687.494020999992
usercpu_time_millis
5754.2060580000225
usercpu_time_millis
5789.806242999986
usercpu_time_millis
5788.164628999964
usercpu_time_millis
5630.090203000009
usercpu_time_millis
5609.618050999985
usercpu_time_millis
5815.891245999978
usercpu_time_millis
5797.045086999987
usercpu_time_millis
5665.105815999994
usercpu_time_millis_testing
37.32713499999818
usercpu_time_millis_testing
41.415656999987505
usercpu_time_millis_testing
39.46574700000838
usercpu_time_millis_testing
39.039390999988655
usercpu_time_millis_testing
40.1866369999766
usercpu_time_millis_testing
38.56854500000395
usercpu_time_millis_testing
39.06189299999596
usercpu_time_millis_testing
39.26834699998949
usercpu_time_millis_testing
38.877896000002465
usercpu_time_millis_testing
39.08026899998163
usercpu_time_millis_training
5559.962428000006
usercpu_time_millis_training
5646.0783640000045
usercpu_time_millis_training
5714.740311000014
usercpu_time_millis_training
5750.766851999998
usercpu_time_millis_training
5747.977991999988
usercpu_time_millis_training
5591.521658000005
usercpu_time_millis_training
5570.5561579999885
usercpu_time_millis_training
5776.622898999989
usercpu_time_millis_training
5758.167190999984
usercpu_time_millis_training
5626.025547000012