6020361
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)
4055046
bootstrap
true
6902
class_weight
null
6902
criterion
"entropy"
6902
max_depth
null
6902
max_features
0.7331455258377714
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
6
6902
min_samples_split
10
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
4164
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
12963845
description
https://api.openml.org/data/download/12963845/description.xml
-1
12963846
predictions
https://api.openml.org/data/download/12963846/predictions.arff
area_under_roc_curve
0.9947687815656565 [0.985361,0.999921,1,1,0.998935,0.994752,0.999684,0.998816,0.997554,0.999921,0.999014,0.999803,0.999487,0.986545,0.999684,0.998658,1,0.983033,0.982521,0.97017,0.999211,0.999605,0.993647,1,0.99854,0.967803,0.988518,0.993253,0.999132,0.999724,0.999961,0.999921,0.998698,0.990057,0.998185,0.999053,0.991872,0.993292,0.998895,0.999842,0.996725,0.999132,1,0.998658,0.99858,0.99929,0.999053,0.996015,0.992306,0.994437,0.996409,0.992503,0.998106,0.985953,0.993056,0.982915,1,0.999645,0.994989,0.980311,1,0.998777,0.996015,0.999132,0.998264,0.995265,0.978338,0.999961,0.989426,0.981731,0.995502,0.998382,0.994358,0.998146,0.963818,0.997869,0.999724,0.985085,0.999842,1,0.992148,0.996567,0.995581,0.995107,0.994318,0.996252,0.999921,0.994634,0.997554,1,0.999842,0.999527,0.997869,0.991359,0.999329,0.999527,0.996567,0.995541,0.965159,0.99929]
average_cost
0
f_measure
0.7749112991389967 [0.642857,0.941176,1,0.969697,0.933333,0.625,0.882353,0.857143,0.714286,0.967742,0.689655,0.903226,0.842105,0.666667,0.941176,0.896552,0.888889,0.6,0.625,0.173913,0.857143,0.933333,0.709677,1,0.857143,0.32,0.352941,0.742857,0.875,0.969697,0.967742,0.941176,0.848485,0.709677,0.8,0.857143,0.545455,0.5,0.909091,0.969697,0.758621,0.8,1,0.888889,0.848485,0.866667,0.866667,0.774194,0.774194,0.875,0.774194,0.758621,0.789474,0.615385,0.6875,0.285714,1,0.969697,0.571429,0.689655,0.914286,0.717949,0.666667,0.810811,0.833333,0.777778,0.368421,0.914286,0.5,0.545455,0.642857,0.8,0.594595,0.8125,0.785714,0.896552,0.941176,0.454545,0.941176,0.967742,0.714286,0.666667,0.685714,0.666667,0.8,0.875,0.969697,0.777778,0.764706,1,0.864865,0.833333,0.75,0.692308,0.857143,0.857143,0.8,0.742857,0.48,0.785714]
kappa
0.7803030303030303
kb_relative_information_score
1141.3261324942798
mean_absolute_error
0.013529750913027407
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
0.7876618754968131 [0.75,0.888889,1,0.941176,1,0.625,0.833333,0.789474,0.833333,1,0.769231,0.933333,0.727273,0.818182,0.888889,1,0.8,0.642857,0.625,0.285714,0.789474,1,0.733333,1,0.789474,0.444444,0.333333,0.684211,0.875,0.941176,1,0.888889,0.823529,0.733333,0.736842,0.789474,0.529412,0.45,0.882353,0.941176,0.846154,0.736842,1,0.8,0.823529,0.928571,0.928571,0.8,0.8,0.875,0.8,0.846154,0.681818,0.8,0.6875,0.333333,1,0.941176,0.526316,0.769231,0.842105,0.608696,0.714286,0.714286,0.75,0.7,0.318182,0.842105,0.75,1,0.75,0.736842,0.52381,0.8125,0.916667,1,0.888889,0.833333,0.888889,1,0.833333,0.714286,0.631579,0.714286,0.736842,0.875,0.941176,0.7,0.722222,1,0.761905,0.75,0.75,0.9,0.789474,0.789474,0.857143,0.684211,0.666667,0.916667]
predictive_accuracy
0.7825
prior_entropy
6.6438561897747395
recall
0.7825 [0.5625,1,1,1,0.875,0.625,0.9375,0.9375,0.625,0.9375,0.625,0.875,1,0.5625,1,0.8125,1,0.5625,0.625,0.125,0.9375,0.875,0.6875,1,0.9375,0.25,0.375,0.8125,0.875,1,0.9375,1,0.875,0.6875,0.875,0.9375,0.5625,0.5625,0.9375,1,0.6875,0.875,1,1,0.875,0.8125,0.8125,0.75,0.75,0.875,0.75,0.6875,0.9375,0.5,0.6875,0.25,1,1,0.625,0.625,1,0.875,0.625,0.9375,0.9375,0.875,0.4375,1,0.375,0.375,0.5625,0.875,0.6875,0.8125,0.6875,0.8125,1,0.3125,1,0.9375,0.625,0.625,0.75,0.625,0.875,0.875,1,0.875,0.8125,1,1,0.9375,0.75,0.5625,0.9375,0.9375,0.75,0.8125,0.375,0.6875]
relative_absolute_error
0.6833207531832011
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.07352181036935428
root_relative_squared_error
0.7389219966751784
total_cost
0
area_under_roc_curve
0.9942291616909482 [1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,1,1,0.968354,0.974684,0.981132,1,1,0.993711,1,1,0.987342,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.968354,1,1,0.993671,1,1,1,1,0.987421,0.981013,1,1,1,1,1,1,0.993671,1,1,0.937107,0.993671,1,0.993671,1,1,1,1,0.977848,1,1,1,1,1,1,0.996835,0.974684,1,0.996835,0.990506,1,1,1,1,0.977848,1,1,1,1,0.848101,1]
area_under_roc_curve
0.9941910974444712 [1,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.955696,1,1,1,0.968354,0.996835,0.987421,1,0.993711,1,1,1,0.955696,0.987342,0.962025,1,1,1,1,1,1,0.996835,1,0.981132,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.952532,1,1,1,0.924528,1,1,1,0.89557,1,1,1,0.996835,1,0.993671,1,1,0.987421,1,1,1,1,1,1,1,1,0.987342,1,1,0.996835,0.993671,0.981132,0.993711,1,1,1,1,0.993671,1,1,1,1,0.990506,1,1,1,0.996835,0.996835,1]
area_under_roc_curve
0.9945075531406736 [1,1,1,1,0.996835,0.987342,1,1,0.993671,1,1,1,1,1,1,1,1,0.993671,0.981132,0.927215,0.993711,1,0.949367,1,0.993711,0.984177,1,1,1,1,1,1,1,0.968354,0.993711,1,0.993711,0.974843,1,1,1,1,1,1,0.996835,1,1,1,0.971519,1,0.996835,1,1,1,1,1,1,1,0.993711,0.981013,1,0.993711,1,1,1,0.990506,0.968354,1,0.974843,0.974684,1,1,0.993711,0.993671,1,1,1,0.981013,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,0.996835,0.996835,0.962264,1]
area_under_roc_curve
0.9945120312873181 [0.993671,0.996835,1,1,0.996835,0.993671,1,1,0.996835,1,1,1,1,1,1,1,1,0.993671,0.849057,0.990506,1,1,0.996835,1,0.987421,0.968354,0.974684,1,1,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.971519,1,0.996835,1,1,1,1,1,1,1,1,0.996835,1,0.955696,1,1,0.958861,0.993671,0.993671,0.981132,1,1,0.990506,1,0.96519,1,1,0.955975,0.996835,1,0.990506,1,1,1,1,1,1,1,1,0.974843,1,1,1,0.993671,0.996835,0.993711,0.996835]
area_under_roc_curve
0.9928904446302045 [0.996835,1,1,1,1,0.993671,1,1,1,1,1,1,1,0.886792,1,1,1,1,1,0.990506,0.993671,1,1,1,1,0.974684,0.984177,1,1,1,1,1,1,0.974684,1,1,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.974843,1,0.993711,0.958861,0.96519,1,0.993671,0.996835,1,1,1,1,1,0.996835,1,0.930818,1,0.971519,0.987342,1,1,1,1,0.81962,1,1,0.996835,1,1,1,1,1,0.996835,0.974843,1,1,1,1,1,1,0.996835,1,0.987421,0.990506,1,0.996835,1,0.987421,0.996835]
area_under_roc_curve
0.9947822128015285 [0.933544,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.920886,1,0.996835,0.996835,1,1,0.993671,0.984177,0.993711,1,1,1,1,0.987342,1,0.987421,1,0.987342,0.993671,1,1,1,0.993671,1,1,1,1,1,1,0.996835,0.971519,1,1,1,1,0.990506,0.981013,1,1,0.993671,1,1,1,0.987421,1,1,0.996835,0.981132,1,0.996835,1,0.993671,0.990506,1,1,0.962025,1,1,1,1,1,1,0.996835,1,0.990506,0.993711,1,1,0.987421,1,1,1,1,1,1,1,1,1,0.974843,1,0.993671]
area_under_roc_curve
0.9960554991640789 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.962025,1,1,1,1,0.993671,0.830189,0.987421,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,0.981013,1,1,1,1,1,1,0.996835,0.993671,1,1,0.993671,0.993711,1,1,1,0.996835,1,0.993711,0.968553,1,0.993671,0.990506,0.993711,1,1,1,1,1,1,0.981132,1,1,0.987342,1,0.993671,0.993671,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,0.971519,1]
area_under_roc_curve
0.9962891091473607 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.974843,0.981013,0.993671,0.996835,1,1,1,1,0.981132,0.987421,0.993671,1,0.996835,1,1,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.993671,1,1,0.981013,1,1,1,1,1,0.996835,0.993711,0.996835,1,0.968553,0.974843,1,0.981013,0.993671,1,1,1,1,0.993671,1,1,0.962264,1,1,0.990506,0.993711,0.977848,1,0.987342,0.993671,1,1,1,1,1,1,1,0.981132,1,1,0.981013,1,0.990506,1]
area_under_roc_curve
0.9973153510866968 [0.981132,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.993711,1,0.996835,1,1,1,1,1,1,1,1,0.993671,1,1,0.974843,1,1,0.990506,0.996835,0.996835,1,1,0.987421,1,1,0.996835,1,0.974684,0.993671,0.974843,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.984177,0.993711,1,0.993711,0.984177,1,1,0.987342,1,1,1,1,1,1,1,0.987421,0.996835,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.993711,1,0.971519,1]
area_under_roc_curve
0.9972800234853911 [0.918239,1,1,1,1,1,1,0.987421,0.996835,1,1,1,1,0.990506,1,1,1,0.993711,0.996835,1,1,1,1,1,1,0.937107,1,1,0.996835,1,1,1,1,0.993711,1,1,0.990506,1,0.996835,1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,1,0.981013,1,1,1,1,0.993671,1,1,1,0.984177,1,0.987421,1,0.996835,1,1,0.993711,0.981132,1,0.987342,1,0.996835,1,1,1,0.993711,1,1,0.981132,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
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
0.8041692987997473
kappa
0.8041770302814956
kappa
0.7410185550730359
kappa
0.7346914603813811
kappa
0.7852094602598018
kappa
0.7346705097326964
kappa
0.7851924975320829
kappa
0.7725387987205308
kappa
0.8041306322315681
kappa
0.8357289527720739
kb_relative_information_score
113.73733900554225
kb_relative_information_score
113.70219738907709
kb_relative_information_score
112.98960522988008
kb_relative_information_score
113.2131614010187
kb_relative_information_score
113.08798092873927
kb_relative_information_score
112.91568048933946
kb_relative_information_score
116.12307085719073
kb_relative_information_score
114.58984516023118
kb_relative_information_score
115.19741410692504
kb_relative_information_score
115.7698379263363
mean_absolute_error
0.013532395173061203
mean_absolute_error
0.013555581374026482
mean_absolute_error
0.013584336811298513
mean_absolute_error
0.013648764860361286
mean_absolute_error
0.013602013969135176
mean_absolute_error
0.013685334161724887
mean_absolute_error
0.01320355127481083
mean_absolute_error
0.013511769489634295
mean_absolute_error
0.013535052602542497
mean_absolute_error
0.013438709413678761
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
0.80625
predictive_accuracy
0.80625
predictive_accuracy
0.74375
predictive_accuracy
0.7375
predictive_accuracy
0.7875
predictive_accuracy
0.7375
predictive_accuracy
0.7875
predictive_accuracy
0.775
predictive_accuracy
0.80625
predictive_accuracy
0.8375
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
0.80625 [1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,1,0,0.5,0,1,1,0,1,1,0.5,0,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,1,0,1]
recall
0.80625 [1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,0,0.5,1,1,0,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,0,1,0,0,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1]
recall
0.74375 [0.5,1,1,1,0.5,0.5,1,1,0,1,0.5,1,1,1,1,0.5,1,1,0,0,1,0,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,0.5,1,0.5,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0,0,0.5,1,0,0.5,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,0,0.5]
recall
0.7375 [0.5,1,1,1,0.5,0,1,1,0.5,1,0,1,1,0.5,1,1,1,0.5,0,0,1,1,0.5,1,0,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,0,0.5,0,0.5,0,1,0,0.5,1,0,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0.5,0.5,1,0.5]
recall
0.7875 [0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0,0,0.5,0,0,0,1,1,1,1,1,1,1,1,1,1,0,1,0.5,0.5,1,1,1,0,0.5,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,0.5,1,0,0.5]
recall
0.7375 [0.5,1,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,0,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,0,1,0.5,1,1,1,1,0,0,1,1,0,1,1,0.5,0,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,0,1,1,0,1,1,1,1,0,1,1,1,1,0,0,0.5]
recall
0.7875 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,0.5,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,0.5,0.5,1,1,0,1,1,1,0.5,0,1,1,0,0,1,0.5,1,0.5,1,1,0,1,1,0,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0.5,1]
recall
0.775 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,0,0.5,0,1,1,1,1,1,0,0,0.5,0,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0,1,1,0,1,1,1,0,1,1,0,0,0,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5]
recall
0.80625 [0,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,0,1,1,0.5,0,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,1,0,1,1,1,1,0,1,1,0.5,0.5,0.5,1,1,1,1,0.5,1]
recall
0.8375 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,1,1,1,0.5,1,0,0,0,1,0.5,1,0,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1]
relative_absolute_error
0.6834543016697566
relative_absolute_error
0.6846253219205282
relative_absolute_error
0.686077616732247
relative_absolute_error
0.6893315586041042
relative_absolute_error
0.6869704024815734
relative_absolute_error
0.6911784930164073
relative_absolute_error
0.6668460239803439
relative_absolute_error
0.6824126004865795
relative_absolute_error
0.683588515279923
relative_absolute_error
0.6787226976605425
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
0.07325430887505628
root_mean_squared_error
0.07343193077979485
root_mean_squared_error
0.07416373114665756
root_mean_squared_error
0.07405219005143844
root_mean_squared_error
0.07388901792677054
root_mean_squared_error
0.07445739245128456
root_mean_squared_error
0.07212830516261513
root_mean_squared_error
0.07356139691850594
root_mean_squared_error
0.07319556398824811
root_mean_squared_error
0.07305672818731972
root_relative_squared_error
0.7362335055011003
root_relative_squared_error
0.7380186728119065
root_relative_squared_error
0.7453735432310825
root_relative_squared_error
0.7442525130445761
root_relative_squared_error
0.7426125714877017
root_relative_squared_error
0.7483249503913673
root_relative_squared_error
0.7249167423898442
root_relative_squared_error
0.739319856463909
root_relative_squared_error
0.735643097174147
root_relative_squared_error
0.7342477448736986
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
7332.708146
usercpu_time_millis
7210.108218999999
usercpu_time_millis
7159.528004999998
usercpu_time_millis
7188.993224000001
usercpu_time_millis
7170.2356969999955
usercpu_time_millis
7171.353439999997
usercpu_time_millis
7178.759101000011
usercpu_time_millis
7190.040081999996
usercpu_time_millis
7193.316967000002
usercpu_time_millis
7172.994336999992
usercpu_time_millis_testing
44.63400499999892
usercpu_time_millis_testing
36.62378499999974
usercpu_time_millis_testing
33.89157400000187
usercpu_time_millis_testing
37.97637899999984
usercpu_time_millis_testing
34.377240999994285
usercpu_time_millis_testing
33.88968199999454
usercpu_time_millis_testing
34.78541200000507
usercpu_time_millis_testing
37.128812999995375
usercpu_time_millis_testing
34.25144899999566
usercpu_time_millis_testing
34.987044999994055
usercpu_time_millis_training
7288.074141000001
usercpu_time_millis_training
7173.484433999999
usercpu_time_millis_training
7125.636430999996
usercpu_time_millis_training
7151.016845000001
usercpu_time_millis_training
7135.858456000001
usercpu_time_millis_training
7137.463758000003
usercpu_time_millis_training
7143.973689000006
usercpu_time_millis_training
7152.911269
usercpu_time_millis_training
7159.065518000006
usercpu_time_millis_training
7138.0072919999975