6016013
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)
4050702
bootstrap
true
6902
class_weight
null
6902
criterion
"entropy"
6902
max_depth
null
6902
max_features
0.2262708840630796
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
10
6902
min_samples_split
9
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
12579
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
12955157
description
https://api.openml.org/data/download/12955157/description.xml
-1
12955158
predictions
https://api.openml.org/data/download/12955158/predictions.arff
area_under_roc_curve
0.9949333175505052 [0.9884,0.999842,1,1,0.999211,0.992464,0.999724,0.999171,0.996133,0.999882,0.998343,1,0.999014,0.982599,0.999132,0.998737,0.999961,0.983783,0.988439,0.971117,0.99779,1,0.993056,1,0.999014,0.968632,0.989189,0.999132,0.997317,0.999921,1,1,0.999487,0.98982,0.99925,0.998658,0.994358,0.990728,0.99854,0.999803,0.997909,0.999132,1,0.999132,0.998935,0.999487,0.999527,0.99708,0.996725,0.997909,0.99562,0.991201,0.99712,0.982757,0.993726,0.976602,1,0.999961,0.993529,0.974274,0.999684,0.998422,0.992345,0.99854,0.999132,0.995344,0.979995,1,0.995423,0.987847,0.993924,0.998777,0.995384,0.999408,0.973485,0.998619,0.999132,0.985164,0.999961,0.999724,0.98982,0.996607,0.998303,0.996133,0.997199,0.996607,0.999961,0.995028,0.99779,0.999921,0.999921,0.999448,0.998501,0.988163,0.999724,0.998737,0.997238,0.994358,0.960938,0.997356]
average_cost
0
f_measure
0.7578433893525908 [0.583333,0.941176,0.969697,1,0.909091,0.611111,0.9375,0.857143,0.583333,0.909091,0.8,0.9375,0.857143,0.545455,0.969697,0.896552,0.842105,0.571429,0.571429,0.16,0.756757,1,0.733333,1,0.823529,0.210526,0.333333,0.769231,0.903226,0.842105,0.888889,1,0.914286,0.606061,0.875,0.731707,0.571429,0.5,0.857143,0.864865,0.685714,0.882353,0.914286,0.909091,0.909091,0.875,0.83871,0.8,0.777778,0.903226,0.714286,0.666667,0.823529,0.56,0.642857,0.090909,1,0.941176,0.594595,0.642857,0.857143,0.823529,0.666667,0.888889,0.820513,0.6875,0.333333,0.914286,0.521739,0.64,0.733333,0.833333,0.666667,0.941176,0.666667,0.933333,0.842105,0.315789,0.969697,0.967742,0.615385,0.774194,0.733333,0.62069,0.666667,0.875,0.941176,0.666667,0.8,0.9375,0.909091,0.744186,0.758621,0.642857,0.875,0.774194,0.625,0.764706,0.363636,0.866667]
kappa
0.7695707070707071
kb_relative_information_score
1013.6517729155421
mean_absolute_error
0.015761581767148394
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
0.780056968753207 [0.875,0.888889,0.941176,1,0.882353,0.55,0.9375,0.789474,0.875,0.882353,0.857143,0.9375,0.789474,1,0.941176,1,0.727273,0.666667,0.526316,0.222222,0.666667,1,0.785714,1,0.777778,0.666667,0.357143,0.652174,0.933333,0.727273,0.8,1,0.842105,0.588235,0.875,0.6,0.526316,0.416667,0.789474,0.761905,0.631579,0.833333,0.842105,0.882353,0.882353,0.875,0.866667,0.857143,0.7,0.933333,0.833333,0.818182,0.777778,0.777778,0.75,0.166667,1,0.888889,0.52381,0.75,0.789474,0.777778,0.818182,0.8,0.695652,0.6875,0.357143,0.842105,0.857143,0.888889,0.785714,0.75,0.517241,0.888889,0.818182,1,0.727273,1,0.941176,1,0.8,0.8,0.785714,0.692308,0.647059,0.875,0.888889,0.565217,0.736842,0.9375,0.882353,0.592593,0.846154,0.75,0.875,0.8,0.625,0.722222,0.666667,0.928571]
predictive_accuracy
0.771875
prior_entropy
6.6438561897747395
recall
0.771875 [0.4375,1,1,1,0.9375,0.6875,0.9375,0.9375,0.4375,0.9375,0.75,0.9375,0.9375,0.375,1,0.8125,1,0.5,0.625,0.125,0.875,1,0.6875,1,0.875,0.125,0.3125,0.9375,0.875,1,1,1,1,0.625,0.875,0.9375,0.625,0.625,0.9375,1,0.75,0.9375,1,0.9375,0.9375,0.875,0.8125,0.75,0.875,0.875,0.625,0.5625,0.875,0.4375,0.5625,0.0625,1,1,0.6875,0.5625,0.9375,0.875,0.5625,1,1,0.6875,0.3125,1,0.375,0.5,0.6875,0.9375,0.9375,1,0.5625,0.875,1,0.1875,1,0.9375,0.5,0.75,0.6875,0.5625,0.6875,0.875,1,0.8125,0.875,0.9375,0.9375,1,0.6875,0.5625,0.875,0.75,0.625,0.8125,0.25,0.8125]
relative_absolute_error
0.7960394831893113
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08221998218198143
root_relative_squared_error
0.8263419126282989
total_cost
0
area_under_roc_curve
0.9942286641190989 [1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,0.987342,1,1,1,0.977848,0.984177,0.987421,1,1,0.987421,1,1,0.984177,0.993671,1,0.996835,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.981013,0.993671,1,1,0.996835,1,1,1,1,0.987421,0.984177,1,1,0.996835,1,1,1,0.984177,1,1,0.949686,0.990506,1,1,1,1,1,1,0.96519,1,1,0.996835,1,1,1,0.996835,0.984177,1,1,0.990506,1,1,1,1,0.984177,1,0.987342,1,0.996835,0.851266,1]
area_under_roc_curve
0.9949411870074039 [0.987421,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.946203,1,1,1,0.955696,0.996835,0.993711,1,1,1,1,1,0.958861,0.993671,1,1,1,1,1,1,1,0.996835,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.962025,1,1,1,0.955975,1,1,1,0.93038,1,1,0.993671,0.987342,1,1,1,1,0.981132,1,1,1,1,1,1,1,1,0.984177,1,0.996835,0.996835,1,0.987421,0.993711,1,1,1,1,1,1,1,1,1,0.974684,1,1,1,0.981013,1,1]
area_under_roc_curve
0.9936776032959157 [1,1,1,1,1,0.993671,1,1,0.993671,1,1,1,1,0.993671,0.990506,1,1,0.990506,0.987421,0.911392,0.993711,1,0.943038,1,1,0.974684,1,1,1,1,1,1,1,0.981013,1,1,0.993711,0.955975,1,1,0.996835,1,1,1,0.996835,1,1,1,0.987342,1,1,1,1,1,1,1,1,1,0.987421,0.936709,1,1,0.996835,1,1,0.987342,0.984177,1,0.981132,0.984177,1,0.996835,0.993711,1,1,1,1,0.977848,1,1,1,0.993671,1,1,1,1,1,1,0.993671,1,1,1,1,0.981013,1,1,1,0.993671,0.937107,1]
area_under_roc_curve
0.9949424309370272 [1,1,1,1,0.996835,0.984177,1,1,0.993671,1,0.996835,1,1,0.993671,1,1,1,1,0.937107,0.993671,1,1,1,1,1,0.981013,0.981013,1,1,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.93038,1,0.977848,1,1,1,0.993671,1,1,0.993671,1,0.996835,1,0.971519,1,1,0.987342,0.984177,1,0.987421,1,0.993711,0.990506,1,0.977848,1,1,0.968553,1,1,0.993671,1,1,1,1,1,1,1,1,0.993711,1,1,0.987421,0.990506,1,0.993711,0.968354]
area_under_roc_curve
0.9932867606082316 [1,1,1,1,1,0.996835,1,0.996835,1,1,0.996835,1,1,0.880503,1,1,1,1,1,0.993671,0.977848,1,1,1,1,0.968354,0.990506,1,1,1,1,1,1,0.977848,1,1,0.996835,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.974843,0.996835,0.993711,0.958861,0.971519,1,0.996835,0.993671,0.993711,1,1,0.987421,1,0.996835,1,0.937107,1,0.984177,0.974684,1,1,1,1,0.898734,1,0.993671,0.971519,1,1,1,1,1,0.993671,0.987421,1,1,1,1,1,1,1,1,0.987421,0.996835,1,0.996835,1,0.981132,0.996835]
area_under_roc_curve
0.994979997611655 [0.958861,1,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.914557,1,1,1,1,0.996835,0.981013,0.981013,0.993711,1,1,1,1,0.990506,0.993671,0.993711,0.996835,0.990506,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.981013,0.977848,1,1,0.993671,1,1,1,0.987421,1,1,1,0.987421,1,1,1,0.977848,0.996835,1,1,0.952532,1,1,0.996835,1,1,1,0.996835,1,0.996835,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,0.987421,0.968553,0.996835]
area_under_roc_curve
0.9960161909879786 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.993711,0.993671,0.971519,1,1,1,1,0.993671,0.867925,0.987421,1,1,1,1,1,1,0.981132,1,1,0.996835,1,1,1,1,0.996835,1,1,1,0.993671,1,0.984177,1,1,1,1,1,1,0.993671,0.990506,1,1,0.990506,0.993711,1,0.996835,1,0.996835,1,0.993711,0.968553,1,1,1,0.993711,1,1,1,0.993671,1,1,0.987421,1,1,0.96519,1,1,1,1,1,1,0.949686,1,1,1,1,0.996835,1,1,1,1,1,0.971519,1]
area_under_roc_curve
0.9964483321391606 [0.996835,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.990506,1,0.987421,0.990506,0.996835,1,1,1,1,1,0.987421,0.968553,1,0.993711,0.996835,1,1,1,1,1,1,0.993671,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,0.955975,1,0.990506,1,1,0.946203,1,1,0.993671,1,1,1,0.993711,1,1,0.974843,0.955975,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.990506,1,0.993671,1,1,0.993711,0.993711,1,1,1,1,0.987421,1,1,0.993671,1,0.987342,1]
area_under_roc_curve
0.9962886115755113 [1,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.987421,0.981132,1,1,1,1,1,1,0.993711,1,1,1,0.974684,1,1,0.981132,1,1,0.996835,1,1,1,1,0.987421,1,0.996835,1,1,0.958861,0.996835,0.955975,1,1,0.993671,0.971519,0.996835,1,1,1,1,0.993711,0.993671,1,0.996835,0.993711,1,0.993711,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.996835,1,0.993671,1,1,0.974843,1,0.949367,1]
area_under_roc_curve
0.997357395907969 [0.955975,1,1,1,1,1,1,0.981132,0.996835,1,1,1,1,0.993671,1,1,1,0.987421,0.996835,0.981132,1,1,1,1,1,0.974843,1,1,0.993671,1,1,1,1,0.987421,1,1,0.996835,1,0.996835,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,1,0.996835,1,1,1,1,0.987342,1,1,1,0.977848,1,0.993711,1,0.996835,1,1,1,0.987421,1,0.993671,1,0.996835,0.996835,1,1,0.993711,1,1,0.974843,1,0.987421,1,1,0.996835,0.990506,1,0.996835,1,1,1,0.993671,1,1,1,1,1,1]
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
0.7031657061656272
kappa
0.7978681405448086
kappa
0.7536809694864406
kappa
0.7473351756810107
kappa
0.785226420308737
kappa
0.7472852912142152
kappa
0.7788658979624072
kappa
0.7599020653161157
kappa
0.791559748924243
kappa
0.829397361977727
kb_relative_information_score
100.23997249385302
kb_relative_information_score
100.62241455317513
kb_relative_information_score
101.37670157083794
kb_relative_information_score
100.6325620867052
kb_relative_information_score
100.5220003877504
kb_relative_information_score
100.76454113190161
kb_relative_information_score
102.7548308555938
kb_relative_information_score
101.92315120195961
kb_relative_information_score
102.38486570421215
kb_relative_information_score
102.43073292955324
mean_absolute_error
0.015811847216295128
mean_absolute_error
0.01583371865859465
mean_absolute_error
0.015725949184601615
mean_absolute_error
0.01585130138365983
mean_absolute_error
0.01577716882371778
mean_absolute_error
0.015847920649256286
mean_absolute_error
0.015628317259338047
mean_absolute_error
0.01566616784235987
mean_absolute_error
0.015722249234953484
mean_absolute_error
0.015751177418707244
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
0.70625
predictive_accuracy
0.8
predictive_accuracy
0.75625
predictive_accuracy
0.75
predictive_accuracy
0.7875
predictive_accuracy
0.75
predictive_accuracy
0.78125
predictive_accuracy
0.7625
predictive_accuracy
0.79375
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
0.70625 [1,1,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,0,0.5,0,1,1,0,1,0,0.5,0,1,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,0,1,1,0,0.5,1,1,0,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,1,0,1,1,0.5,1,0,1,1,0.5,1,1,0,1,1,1,0.5,0.5,0.5,0,1,1,0,1]
recall
0.8 [0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0,1,0,0,1,1,1,1,1,1,1,0,1,0.5,0,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0,1,1,0,0.5,0.5,1]
recall
0.75625 [0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,0.5,1,0.5,0,0,0,1,0,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0,1,1,0.5,0.5,1,0,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,0,1]
recall
0.75 [1,1,1,1,0.5,0,1,1,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0.5,1,0,0.5,0,1,1,1,1,0.5,1,0,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,0.5,1,0,1,1,0,0.5,1,0,0]
recall
0.7875 [0.5,1,1,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,0,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,0.5,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5]
recall
0.75 [0,1,1,1,1,0.5,1,1,1,0.5,0,1,0,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,0,0,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,0,1,0.5,1,1,0,0,1]
recall
0.78125 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,0,0.5,0,1,1,1,1,0.5,0,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0,0.5,0,1,1,0.5,1,0.5,0.5,1,1,1,1,0,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.7625 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,1,0,1,1,0.5,1,1,0,0,1,0,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,0,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,0,1,0,1,1,1,1,1,0,1,1,0,1,1,0,1,0,1,0,0.5,1,0,1,1,1,1,1,0,1,1,0.5,1,0,1]
recall
0.79375 [0,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,0.5,1,1,1,0,1,0.5,0.5,1,0.5,0,0,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0,1,0.5,1]
recall
0.83125 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,1,0,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,0,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1]
relative_absolute_error
0.7985781422371264
relative_absolute_error
0.799682760535082
relative_absolute_error
0.7942398578081611
relative_absolute_error
0.8005707769525153
relative_absolute_error
0.7968267082685735
relative_absolute_error
0.8004000327907203
relative_absolute_error
0.7893089524918193
relative_absolute_error
0.791220598098982
relative_absolute_error
0.7940529916643161
relative_absolute_error
0.7955140110458191
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
0.08247525998329107
root_mean_squared_error
0.08247228700001312
root_mean_squared_error
0.08212549758877002
root_mean_squared_error
0.08270731569438938
root_mean_squared_error
0.08236693104607742
root_mean_squared_error
0.08269331690832243
root_mean_squared_error
0.0815997314424048
root_mean_squared_error
0.08178211030395054
root_mean_squared_error
0.08189202260135123
root_mean_squared_error
0.08207729009857187
root_relative_squared_error
0.8289075510654231
root_relative_squared_error
0.8288776714592384
root_relative_squared_error
0.8253923067369306
root_relative_squared_error
0.83123979871443
root_relative_squared_error
0.8278188042815576
root_relative_squared_error
0.83109910562078
root_relative_squared_error
0.8201081581461289
root_relative_squared_error
0.8219411346717028
root_relative_squared_error
0.8230457948241999
root_relative_squared_error
0.8249078032306518
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
2214.947795997432
usercpu_time_millis
2075.140388998989
usercpu_time_millis
2080.337669001892
usercpu_time_millis
2073.997033003252
usercpu_time_millis
2067.0972510015417
usercpu_time_millis
2059.86275900068
usercpu_time_millis
2062.6896590001706
usercpu_time_millis
2063.6692450025294
usercpu_time_millis
2119.043146001786
usercpu_time_millis
2061.5033829963068
usercpu_time_millis_testing
32.819996999023715
usercpu_time_millis_testing
33.092552999733016
usercpu_time_millis_testing
33.37876800287631
usercpu_time_millis_testing
33.30232100051944
usercpu_time_millis_testing
32.82488500190084
usercpu_time_millis_testing
32.7671660015767
usercpu_time_millis_testing
32.806130999233574
usercpu_time_millis_testing
32.869538001250476
usercpu_time_millis_testing
32.89218899953994
usercpu_time_millis_testing
32.924258997809375
usercpu_time_millis_training
2182.1277989984083
usercpu_time_millis_training
2042.0478359992558
usercpu_time_millis_training
2046.958900999016
usercpu_time_millis_training
2040.6947120027326
usercpu_time_millis_training
2034.272365999641
usercpu_time_millis_training
2027.0955929991032
usercpu_time_millis_training
2029.883528000937
usercpu_time_millis_training
2030.7997070012789
usercpu_time_millis_training
2086.1509570022463
usercpu_time_millis_training
2028.5791239984974