6683190
1
Jan van Rijn
9954
Supervised Classification
7096
sklearn.model_selection._search.RandomizedSearchCV(estimator=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)
4715522
bootstrap
true
6902
class_weight
null
6902
criterion
"entropy"
6902
max_depth
null
6902
max_features
"auto"
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
1
6902
min_samples_split
2
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
10
6902
n_jobs
1
6902
oob_score
false
6902
random_state
1
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
true
6948
threshold
0.0
6949
cv
null
7096
error_score
"raise"
7096
fit_params
{}
7096
iid
true
7096
n_iter
50
7096
n_jobs
-1
7096
param_distributions
{"classifier__min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__max_features": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], "classifier__min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__criterion": ["gini", "entropy"], "imputation__strategy": ["mean", "median", "most_frequent"]}
7096
pre_dispatch
"2*n_jobs"
7096
random_state
1
7096
refit
true
7096
return_train_score
true
7096
scoring
null
7096
verbose
0
7096
openml-python
Sklearn_0.18.1.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
14297181
description
https://api.openml.org/data/download/14297181/description.xml
-1
14297182
predictions
https://api.openml.org/data/download/14297182/predictions.arff
-1
14297183
trace
https://api.openml.org/data/download/14297183/trace.arff
area_under_roc_curve
0.9805916587752529 [0.923414,0.999566,0.999882,1,0.99856,0.993213,0.99779,0.998856,0.995896,0.999645,0.996173,0.999211,0.998244,0.938763,0.964548,0.991083,0.999803,0.976562,0.982264,0.934324,0.997277,0.99781,0.982836,0.999467,0.993845,0.870758,0.981929,0.989564,0.982876,0.998757,0.999625,0.999744,0.961174,0.95709,0.994298,0.997948,0.979818,0.93894,0.995403,0.999527,0.987906,0.996113,0.998264,0.997435,0.996607,0.992681,0.995088,0.994456,0.984789,0.99343,0.985105,0.983329,0.928228,0.936257,0.98767,0.966856,1,0.996252,0.99345,0.933712,0.999921,0.963443,0.986506,0.994851,0.996982,0.995956,0.933101,0.998777,0.935784,0.878216,0.98039,0.997001,0.982225,0.956242,0.953105,0.996271,0.997711,0.909308,0.999665,0.999645,0.990116,0.995541,0.98404,0.964035,0.993963,0.961746,0.999605,0.984513,0.994575,0.999132,0.994456,0.999448,0.995009,0.986308,0.994338,0.995186,0.949455,0.95859,0.917732,0.992168]
average_cost
0
f_measure
0.6622919142549043 [0.357143,0.909091,0.882353,1,0.823529,0.444444,0.848485,0.742857,0.722222,0.967742,0.645161,0.888889,0.882353,0.615385,0.733333,0.709677,0.842105,0.5,0.4375,0.24,0.685714,0.933333,0.62069,0.9375,0.648649,0.142857,0.322581,0.606061,0.645161,0.75,0.857143,0.866667,0.823529,0.484848,0.774194,0.810811,0.344828,0.342857,0.823529,0.848485,0.733333,0.722222,0.764706,0.709677,0.8,0.6875,0.75,0.714286,0.625,0.6875,0.514286,0.606061,0.625,0.466667,0.6,0.214286,0.969697,0.764706,0.666667,0.466667,0.909091,0.774194,0.529412,0.702703,0.628571,0.666667,0.30303,0.756757,0.166667,0.363636,0.62069,0.774194,0.411765,0.62069,0.461538,0.787879,0.756757,0.307692,0.888889,0.896552,0.625,0.689655,0.625,0.666667,0.606061,0.857143,0.896552,0.631579,0.8,0.8,0.727273,0.756757,0.705882,0.4,0.75,0.722222,0.533333,0.611111,0.214286,0.733333]
kappa
0.6679292929292929
kb_relative_information_score
1115.9976040515592
mean_absolute_error
0.013271712422429534
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
0.666780655017497 [0.416667,0.882353,0.833333,1,0.777778,0.545455,0.823529,0.684211,0.65,1,0.666667,0.8,0.833333,0.8,0.785714,0.733333,0.727273,0.45,0.4375,0.333333,0.631579,1,0.692308,0.9375,0.571429,0.166667,0.333333,0.588235,0.666667,0.75,0.789474,0.928571,0.777778,0.470588,0.8,0.714286,0.384615,0.315789,0.777778,0.823529,0.785714,0.65,0.722222,0.733333,0.857143,0.6875,0.75,0.833333,0.625,0.6875,0.473684,0.588235,0.625,0.5,0.642857,0.25,0.941176,0.722222,0.6,0.5,0.882353,0.8,0.5,0.619048,0.578947,0.714286,0.294118,0.666667,0.25,0.666667,0.692308,0.8,0.388889,0.692308,0.6,0.764706,0.666667,0.4,0.8,1,0.625,0.769231,0.625,0.647059,0.588235,1,1,0.545455,0.736842,0.736842,0.705882,0.666667,0.666667,0.555556,0.75,0.65,0.571429,0.55,0.25,0.785714]
predictive_accuracy
0.67125
prior_entropy
6.6438561897747395
recall
0.67125 [0.3125,0.9375,0.9375,1,0.875,0.375,0.875,0.8125,0.8125,0.9375,0.625,1,0.9375,0.5,0.6875,0.6875,1,0.5625,0.4375,0.1875,0.75,0.875,0.5625,0.9375,0.75,0.125,0.3125,0.625,0.625,0.75,0.9375,0.8125,0.875,0.5,0.75,0.9375,0.3125,0.375,0.875,0.875,0.6875,0.8125,0.8125,0.6875,0.75,0.6875,0.75,0.625,0.625,0.6875,0.5625,0.625,0.625,0.4375,0.5625,0.1875,1,0.8125,0.75,0.4375,0.9375,0.75,0.5625,0.8125,0.6875,0.625,0.3125,0.875,0.125,0.25,0.5625,0.75,0.4375,0.5625,0.375,0.8125,0.875,0.25,1,0.8125,0.625,0.625,0.625,0.6875,0.625,0.75,0.8125,0.75,0.875,0.875,0.75,0.875,0.75,0.3125,0.75,0.8125,0.5,0.6875,0.1875,0.6875]
relative_absolute_error
0.6702885061833085
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.07538311238816858
root_relative_squared_error
0.7576287858204452
total_cost
0
area_under_roc_curve
0.9819143828118783 [0.981132,1,1,1,1,1,1,1,1,1,1,1,1,0.906646,1,1,1,0.990506,0.984177,0.962264,0.993711,1,0.981132,1,1,0.974684,0.990506,1,0.946203,0.993711,1,1,1,0.987342,1,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.982595,0.987342,1,1,0.971519,1,0.943396,1,1,1,0.924051,1,1,0.984177,1,1,1,0.976266,1,1,0.374214,0.990506,0.993711,0.990506,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.988924,0.718354,1,1,0.992089,1,1,0.993711,1,0.984177,0.996835,0.993671,1,1,0.696203,1]
area_under_roc_curve
0.9846818774381021 [0.418239,1,1,1,1,1,0.990506,1,1,1,1,1,0.996835,0.713608,1,1,1,0.981013,0.981013,0.981132,1,1,1,1,1,0.721519,0.993671,0.990506,0.990506,1,1,1,1,1,1,0.981132,0.993711,1,0.993711,1,1,1,1,0.996835,1,0.990506,1,1,0.976266,1,1,0.933544,0.993711,1,1,0.981132,1,1,1,0.995253,1,1,0.993671,1,1,0.993671,0.996835,1,0.943396,0.993711,1,0.993711,1,0.996835,0.993711,1,1,0.97943,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.996835,0.976266,0.981013,1,0.981132,0.987342,1,1]
area_under_roc_curve
0.9729976464851523 [0.987342,1,1,1,1,0.993671,1,1,0.993671,1,1,1,1,1,0.72943,0.993671,0.996835,0.947785,1,0.702532,1,1,0.952532,1,0.993711,0.976266,1,1,0.981013,1,1,1,1,0.996835,0.962264,1,0.993711,0.924528,1,1,0.962025,1,1,1,0.998418,1,0.996835,1,0.995253,1,1,1,1,0.984177,1,0.993671,1,1,0.981132,0.71519,1,1,0.971519,1,1,0.984177,0.981013,1,0.871069,0.707278,1,0.993671,0.981132,1,0.981132,1,1,0.688291,1,1,1,0.996835,0.876582,0.996835,1,1,1,0.971519,1,1,0.993671,1,0.974843,0.974684,1,1,0.996835,1,0.955975,1]
area_under_roc_curve
0.9835809997213598 [0.943038,0.993671,1,1,0.998418,0.974684,1,1,0.993671,1,0.996835,1,1,1,1,1,1,0.988924,0.955975,0.984177,1,1,0.981013,1,0.937107,0.990506,0.962025,0.993711,0.996835,1,1,0.993671,1,0.990506,0.943396,1,0.987421,1,1,1,1,0.993711,1,1,1,1,1,1,0.955696,1,1,0.993711,0.987421,0.718354,1,0.987342,1,0.993671,1,0.990506,1,0.993711,1,1,0.993671,1,0.94462,0.984177,0.993711,0.996835,0.919304,1,0.861635,1,0.987421,1,1,0.685127,1,1,1,1,0.993671,1,1,0.993711,1,1,0.984177,1,1,1,1,0.996835,1,0.981132,0.993671,0.996835,0.968553,0.977848]
area_under_roc_curve
0.966735953546692 [0.971519,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,0.833333,1,0.987342,1,0.996835,0.987421,0.908228,1,1,1,1,1,0.707278,0.958861,1,1,1,1,1,0.990506,0.713608,1,1,1,0.962025,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,0.981132,0.726266,0.883648,0.974684,0.996835,1,0.993671,1,0.943396,1,1,1,0.974843,0.996835,0.996835,0.91195,1,0.697785,0.697785,0.984177,1,0.993711,0.993711,0.726266,1,0.987342,0.984177,1,1,1,1,1,0.726266,0.987421,1,1,1,1,1,1,1,0.974843,0.955975,0.974684,1,0.987342,1,0.993711,1]
area_under_roc_curve
0.9808097733062653 [0.931962,1,1,1,1,0.993671,1,1,1,0.998418,1,0.996835,1,0.987421,1,1,1,0.943038,0.993711,0.933544,1,0.996835,0.984177,1,0.996835,0.977848,0.96519,0.987421,1,1,1,1,0.716772,1,0.990566,1,0.971519,0.971519,1,1,1,0.992089,1,0.987421,0.993711,1,1,0.996835,0.955696,0.995253,1,0.993711,0.996835,1,0.996835,0.974684,1,0.996835,0.996835,1,1,0.71519,0.981132,1,0.998418,1,0.987421,1,0.981013,0.987342,0.984177,1,0.993711,1,0.996835,1,1,0.990506,1,0.987421,1,0.990506,1,1,0.993711,1,1,0.955975,0.993671,1,1,1,1,1,1,1,1,0.459119,0.836478,0.982595]
area_under_roc_curve
0.9881855296154767 [0.962025,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,0.984177,0.987342,0.996835,1,0.993671,1,0.990506,0.430818,1,1,0.993711,0.996835,0.996835,1,1,0.937107,1,1,0.974684,0.962025,1,1,1,1,1,1,0.987421,0.969937,0.957278,0.962025,1,1,0.993711,1,1,1,0.987342,0.941456,1,0.996835,0.984177,1,1,1,1,0.996835,0.996835,1,0.968553,1,0.990506,0.990506,0.993711,1,0.990506,1,1,1,1,0.987421,1,1,0.981013,1,0.996835,1,1,0.984177,1,0.889937,1,0.996835,1,1,1,0.993711,1,0.993671,0.990506,1,0.927215,1]
area_under_roc_curve
0.9722147171801605 [1,1,1,1,1,0.993671,1,1,1,1,0.987421,0.993711,0.996835,1,0.993711,0.943038,1,1,0.968354,0.984177,0.990506,1,1,1,0.996835,0.943396,1,0.976266,0.937107,0.996835,1,1,1,1,0.996835,1,0.962025,0.726266,1,1,0.993671,1,1,1,0.981132,0.996835,1,1,1,1,0.937107,0.993671,0.727848,1,0.993671,0.936709,1,1,0.996835,0.987421,1,0.977848,0.993711,1,1,0.968553,0.41195,1,0.976266,0.949367,1,1,0.996835,0.430818,0.996835,1,1,0.955975,1,1,0.949367,1,1,0.996835,0.971519,1,1,1,0.993711,1,0.962264,1,1,0.993711,0.987421,1,0.623418,0.95283,0.984177,0.977848]
area_under_roc_curve
0.9897712910994348 [0.937107,1,1,1,1,1,1,1,0.990506,1,0.974843,1,1,1,1,1,1,1,1,1,1,0.981013,0.927673,0.998418,1,0.993711,0.981132,1,0.971519,1,1,1,1,1,1,1,0.981013,0.96519,1,1,0.949686,0.996835,1,0.984177,1,0.996835,1,1,0.987421,1,0.952532,0.968354,1,0.939873,0.977848,0.943396,1,1,1,0.914557,1,1,0.96519,0.981013,1,1,0.981013,0.987421,0.990506,1,1,0.981132,0.962025,0.996835,0.974684,0.993671,1,0.962264,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,0.996835,1,1,0.993671,1,0.974684,0.962264,0.996835,0.968354,1]
area_under_roc_curve
0.9899925861794442 [0.823899,1,1,1,1,1,1,0.987421,0.996835,1,0.993711,1,1,0.974684,1,1,1,0.987421,0.996835,0.968553,0.996835,1,1,1,1,0.924528,0.987421,0.962025,1,1,1,1,0.981132,0.993711,1,0.993671,0.962025,0.987342,0.973101,1,1,0.987342,0.996835,1,1,0.995253,1,0.982595,1,1,0.990506,1,1,0.957278,0.984177,0.962264,1,1,0.984177,1,1,1,0.968354,0.996835,0.987421,1,0.996835,1,0.993671,1,0.993711,1,0.971519,0.952532,0.966772,0.977848,1,0.981132,1,1,0.993671,0.971698,0.971698,0.984277,1,1,1,1,1,1,1,1,0.982595,0.993671,1,1,0.987421,1,0.939873,1]
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
0.7220247966516623
kappa
0.7409674234945706
kappa
0.6526818486797964
kappa
0.6210626036156943
kappa
0.6336793905182962
kappa
0.6463390566410104
kappa
0.6967423494570582
kappa
0.646269245953415
kappa
0.67156166114006
kappa
0.6463390566410104
kb_relative_information_score
115.22713425523743
kb_relative_information_score
115.92980553271641
kb_relative_information_score
110.17188905388647
kb_relative_information_score
111.11437045132834
kb_relative_information_score
110.01986985016873
kb_relative_information_score
110.90828410603302
kb_relative_information_score
114.09418803071866
kb_relative_information_score
110.13700814887183
kb_relative_information_score
110.04205304705911
kb_relative_information_score
108.353001575541
mean_absolute_error
0.012727139041514079
mean_absolute_error
0.012874963237333479
mean_absolute_error
0.01333646218875007
mean_absolute_error
0.013550121129891003
mean_absolute_error
0.0130189825268688
mean_absolute_error
0.01342552230245259
mean_absolute_error
0.012742161090542733
mean_absolute_error
0.013085628324901783
mean_absolute_error
0.013813569803841385
mean_absolute_error
0.01414257457819961
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
0.725
predictive_accuracy
0.74375
predictive_accuracy
0.65625
predictive_accuracy
0.625
predictive_accuracy
0.6375
predictive_accuracy
0.65
predictive_accuracy
0.7
predictive_accuracy
0.65
predictive_accuracy
0.675
predictive_accuracy
0.65
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
0.725 [0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0.5,1,1,0,0.5,0,1,1,0,1,1,0,0.5,1,0,0,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,0,1,1,1,0,1,1,0,1,1,1,0.5,1,1,0,0,1,1,1,0,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,0,1,0.5,1,0.5,1,1,0,1]
recall
0.74375 [0,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,0,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,0,0,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,0,0,1,0,1,0,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,0,0,1,0,0.5,0.5,1]
recall
0.65625 [0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,0,0,1,0,1,0,1,1,0,1,1,0.5,0.5,1,0.5,1,1,1,1,0.5,0,1,1,0,1,0,0,1,1,1,1,1,0.5,1,0,1,0.5,1,0,0.5,1,0,1,1,0,0.5,1,1,0.5,1,1,0.5,0.5,1,0,0,1,1,0,1,1,1,1,0,1,1,1,0.5,0,0,1,1,1,0.5,1,1,0.5,1,0,0,1,1,1,1,0,0.5]
recall
0.625 [0.5,0.5,1,1,0.5,0,1,0.5,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0,0.5,1,1,0.5,1,0,0,0,0,1,1,1,0.5,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0,1,0,0,0.5,1,0.5,1,0,1,0,0,0,1,1,1,1,0,0.5,0,1,0,0.5,0,0.5,0,0.5,1,0,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,0,0.5,0.5,0,0.5]
recall
0.6375 [0,1,0.5,1,1,0,1,0.5,1,1,1,1,1,0,1,0.5,1,1,0,0,0.5,1,1,1,1,0,0.5,1,1,0.5,1,0,0.5,0,0,1,0.5,0,0.5,1,0.5,0.5,1,1,1,1,1,0,1,1,0,0,0.5,0,0.5,0.5,1,1,1,0,1,1,1,0,0.5,0,0,1,0,0,0.5,1,1,0,0,1,0,0,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0,0.5,1,0.5,1,1,1]
recall
0.65 [0,1,1,1,1,0,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0,0.5,1,0.5,1,0.5,0,0,0,1,1,1,1,0.5,0.5,0,1,0.5,0,1,1,1,0.5,1,0,0,1,1,0,0.5,0.5,1,1,0.5,1,0.5,0,1,1,0.5,1,1,0.5,0,1,0.5,1,1,1,0,0,0.5,1,0,1,0,1,1,0.5,1,1,1,0,1,1,0,1,1,0,1,0.5,1,1,1,1,1,1,1,0,0,0.5]
recall
0.7 [0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,0.5,1,1,0.5,1,0.5,0,0,1,1,0.5,0.5,0,1,0,1,1,0.5,0.5,1,1,0.5,1,1,0.5,0,0.5,0,0.5,1,1,0,1,1,0,0.5,0.5,1,0.5,0.5,1,1,1,1,0.5,0,0,0,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,0.5,1,1,0.5,0,0,1,0.5,1,0,1]
recall
0.65 [1,1,1,1,1,0,1,1,1,1,0,1,1,0,0,0.5,1,1,0.5,0,0.5,1,1,1,0.5,0,0,0.5,0,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0.5,1,1,1,0,0,0.5,0.5,1,0.5,0,1,1,1,1,1,0.5,1,1,1,0,0,1,0,0,1,1,0.5,0,0.5,1,1,0,1,1,0,0,0,0.5,0,0.5,1,1,1,1,0,1,1,0,1,1,0,0,0,0.5]
recall
0.675 [0,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,0,0,0.5,1,0,0,0.5,0.5,1,1,1,1,1,1,1,0.5,0,1,1,0,1,0,0.5,0.5,0.5,1,1,0,1,0,0.5,1,0.5,0.5,0,1,1,1,0,1,1,0.5,0.5,1,1,0.5,0,0,1,1,0,0.5,0.5,0,1,1,0,1,0.5,1,1,0,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,0,1,0.5,0]
recall
0.65 [0,1,1,1,1,0,1,0,1,1,1,1,1,0.5,1,1,1,1,0,0,0.5,1,1,1,1,0,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0.5,1,1,0.5,0.5,1,1,0.5,1,0.5,1,1,0.5,1,1,0,0,0,1,1,0.5,1,1,0.5,0,1,0,1,0,1,0,1,0,0,0,0.5,0.5,0,1,0,1,0.5,0.5,0,0,0,1,1,0,1,1,1,1,1,0,0.5,1,1,0,0.5,0,1]
relative_absolute_error
0.6427848000764677
relative_absolute_error
0.6502506685521948
relative_absolute_error
0.6735586964015176
relative_absolute_error
0.6843495520146959
relative_absolute_error
0.6575243700438778
relative_absolute_error
0.6780566819420488
relative_absolute_error
0.6435434894213492
relative_absolute_error
0.6608903194394828
relative_absolute_error
0.6976550405980487
relative_absolute_error
0.7142714433434135
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
0.07255997004331412
root_mean_squared_error
0.0731440918022139
root_mean_squared_error
0.07602052648095298
root_mean_squared_error
0.07669325725491934
root_mean_squared_error
0.07487400888565814
root_mean_squared_error
0.07625015605951628
root_mean_squared_error
0.07302417465949407
root_mean_squared_error
0.07521549246766851
root_mean_squared_error
0.07692977292570571
root_mean_squared_error
0.07887876076764681
root_relative_squared_error
0.7292551376760624
root_relative_squared_error
0.7351257822401626
root_relative_squared_error
0.7640350384927201
root_relative_squared_error
0.770796237165968
root_relative_squared_error
0.7525121031014064
root_relative_squared_error
0.7663429025923016
root_relative_squared_error
0.73392056960885
root_relative_squared_error
0.7559441422335124
root_relative_squared_error
0.773173309096385
root_relative_squared_error
0.7927613739226974
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