6668747
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)
4694701
bootstrap
true
6902
class_weight
null
6902
criterion
"gini"
6902
max_depth
null
6902
max_features
0.5
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
"median"
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__bootstrap": [true, false], "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
14267526
description
https://api.openml.org/data/download/14267526/description.xml
-1
14267527
predictions
https://api.openml.org/data/download/14267527/predictions.arff
-1
14267528
trace
https://api.openml.org/data/download/14267528/trace.arff
area_under_roc_curve
0.9752687026515151 [0.982442,0.999645,0.966363,1,0.998343,0.960819,0.998915,0.997218,0.997258,0.999842,0.996903,0.965949,0.998106,0.92515,0.998994,0.990155,0.999744,0.953776,0.94693,0.94553,0.996705,0.996488,0.995699,1,0.998441,0.878117,0.874921,0.963778,0.964568,0.997929,0.999842,1,0.998323,0.957327,0.897747,0.997929,0.985855,0.949495,0.966146,0.998639,0.993608,0.997001,0.999566,0.998402,0.965159,0.964134,0.966659,0.998363,0.992602,0.96078,0.919429,0.994358,0.992424,0.941821,0.952494,0.971216,1,0.966442,0.99491,0.956873,0.999941,0.964489,0.96218,0.997258,0.994614,0.990313,0.933081,0.999921,0.918975,0.951192,0.990767,0.998086,0.958018,0.965593,0.95565,0.996015,0.999507,0.918837,0.999211,0.998501,0.925939,0.996212,0.994338,0.961924,0.959399,0.960997,0.999527,0.993726,0.964094,0.999645,0.999171,0.998915,0.997711,0.99418,0.993154,0.964765,0.996232,0.963048,0.808771,0.996705]
average_cost
0
f_measure
0.6972179845441278 [0.444444,0.848485,0.882353,0.969697,0.83871,0.48,0.875,0.742857,0.727273,0.933333,0.642857,0.83871,0.705882,0.615385,0.933333,0.764706,0.810811,0.645161,0.294118,0.071429,0.6875,0.896552,0.702703,1,0.769231,0.275862,0.216216,0.756757,0.714286,0.827586,0.909091,1,0.8125,0.5,0.764706,0.6875,0.344828,0.470588,0.8125,0.8,0.709677,0.787879,0.9375,0.769231,0.83871,0.75,0.866667,0.787879,0.774194,0.8125,0.645161,0.588235,0.647059,0.363636,0.571429,0.324324,1,0.882353,0.611111,0.62069,0.969697,0.736842,0.645161,0.647059,0.764706,0.4375,0.307692,0.914286,0.413793,0.37037,0.571429,0.774194,0.606061,0.903226,0.647059,0.866667,0.848485,0.461538,0.823529,0.83871,0.571429,0.606061,0.5625,0.689655,0.733333,0.827586,0.9375,0.588235,0.647059,0.9375,0.857143,0.848485,0.684211,0.689655,0.62069,0.764706,0.666667,0.631579,0.25,0.758621]
kappa
0.6988636363636364
kb_relative_information_score
1185.267506355841
mean_absolute_error
0.011682889603451978
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
0.7042747079015418 [0.545455,0.823529,0.833333,0.941176,0.866667,0.666667,0.875,0.684211,0.705882,1,0.75,0.866667,0.666667,0.8,1,0.722222,0.714286,0.666667,0.277778,0.083333,0.6875,1,0.619048,1,0.652174,0.307692,0.190476,0.666667,0.833333,0.923077,0.882353,1,0.8125,0.583333,0.722222,0.6875,0.384615,0.444444,0.8125,0.736842,0.733333,0.764706,0.9375,0.652174,0.866667,0.75,0.928571,0.764706,0.8,0.8125,0.666667,0.555556,0.611111,0.352941,0.666667,0.285714,1,0.833333,0.55,0.692308,0.941176,0.636364,0.666667,0.611111,0.722222,0.4375,0.4,0.842105,0.461538,0.454545,0.666667,0.8,0.588235,0.933333,0.611111,0.928571,0.823529,0.6,0.777778,0.866667,0.526316,0.588235,0.5625,0.769231,0.785714,0.923077,0.9375,0.555556,0.611111,0.9375,0.789474,0.823529,0.590909,0.769231,0.692308,0.722222,0.647059,0.545455,0.375,0.846154]
predictive_accuracy
0.701875
prior_entropy
6.6438561897747395
recall
0.701875 [0.375,0.875,0.9375,1,0.8125,0.375,0.875,0.8125,0.75,0.875,0.5625,0.8125,0.75,0.5,0.875,0.8125,0.9375,0.625,0.3125,0.0625,0.6875,0.8125,0.8125,1,0.9375,0.25,0.25,0.875,0.625,0.75,0.9375,1,0.8125,0.4375,0.8125,0.6875,0.3125,0.5,0.8125,0.875,0.6875,0.8125,0.9375,0.9375,0.8125,0.75,0.8125,0.8125,0.75,0.8125,0.625,0.625,0.6875,0.375,0.5,0.375,1,0.9375,0.6875,0.5625,1,0.875,0.625,0.6875,0.8125,0.4375,0.25,1,0.375,0.3125,0.5,0.75,0.625,0.875,0.6875,0.8125,0.875,0.375,0.875,0.8125,0.625,0.625,0.5625,0.625,0.6875,0.75,0.9375,0.625,0.6875,0.9375,0.9375,0.875,0.8125,0.625,0.5625,0.8125,0.6875,0.75,0.1875,0.6875]
relative_absolute_error
0.5900449294672705
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.07037580205735258
root_relative_squared_error
0.7073034234683634
total_cost
0
area_under_roc_curve
0.9599567858848819 [0.993711,1,1,1,1,1,1,1,1,1,0.996835,0.743671,1,0.996835,1,1,1,0.943038,0.968354,0.908805,1,1,0.987421,1,0.993711,0.966772,0.977848,1,0.727848,1,0.990506,1,0.993671,0.987342,1,0.987421,1,0.981132,1,1,1,1,1,0.995253,1,1,1,1,1,0.731013,0.943038,1,1,0.981013,1,0.990566,1,1,0.993711,1,1,1,0.996835,1,1,0.993671,0.963608,1,1,0.987421,0.949367,1,0.996835,1,1,0.993671,1,0.718354,1,1,0.996835,0.996835,0.968553,1,0.718354,0.731013,1,0.996835,0.727848,1,1,1,1,0.993671,1,0.731013,1,0.996835,0.455696,1]
area_under_roc_curve
0.9723577690868564 [1,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,0.72943,1,1,1,0.723101,0.993671,0.981132,1,0.993711,1,1,1,0.721519,0.987342,0.719937,1,1,1,1,1,1,0.738924,0.993711,0.974843,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.984177,0.933962,0.968553,1,1,1,0.990506,1,1,1,0.996835,1,1,1,1,0.987421,1,1,1,1,0.993671,1,1,1,0.969937,1,0.990506,0.718354,0.993671,0.974843,0.993711,0.996835,1,1,0.996835,1,1,1,1,0.996835,0.992089,0.977848,1,0.993711,0.738924,0.96519,1]
area_under_roc_curve
0.972841160138524 [1,1,1,1,0.993671,0.977848,1,0.990506,0.998418,1,1,1,1,1,0.995253,0.988924,1,0.996835,0.908805,0.974684,0.996855,1,0.996835,1,0.990566,0.990506,0.889241,1,1,1,1,1,1,0.726266,0.443396,1,0.993711,0.443396,1,1,1,1,1,1,0.993671,1,0.743671,1,0.993671,1,0.990506,1,1,0.971519,1,0.985759,1,1,0.987421,0.992089,1,0.987421,0.996835,1,1,0.984177,0.955696,1,0.443396,0.987342,0.996835,1,0.990566,0.996835,0.959119,1,1,0.713608,0.995253,1,1,0.996835,0.996835,1,1,1,1,0.993671,0.996835,1,0.996835,1,0.987421,0.995253,1,1,0.996835,1,0.943396,1]
area_under_roc_curve
0.9841629099992039 [0.984177,0.998418,1,1,0.996835,0.732595,1,0.998418,0.985759,1,0.996835,0.996835,1,1,1,1,1,0.974684,0.937107,0.96519,1,1,0.996835,1,1,0.962025,0.727848,1,1,1,1,1,1,0.974684,1,0.993711,0.993711,0.987421,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993671,1,1,0.971519,1,0.988924,1,1,1,1,1,1,0.993671,1,0.993671,1,0.949367,1,1,0.719937,0.97943,0.992089,0.968553,1,0.993711,0.974684,1,0.993671,1,1,1,1,1,0.996835,1,1,1,0.990506,1,1,0.996835,1,1,1,1,0.987421,1,1,1,0.97943]
area_under_roc_curve
0.9788322983838864 [0.984177,1,1,1,1,0.996835,0.993711,1,1,1,0.993671,1,1,0.455975,1,0.995253,1,1,1,0.993671,1,0.996835,1,1,1,0.977848,0.982595,1,0.993711,1,1,1,1,0.977848,1,1,0.977848,0.969937,1,1,1,1,0.993711,0.993711,1,1,1,1,1,0.996835,0.993711,0.949686,1,0.933962,0.969937,0.879747,1,0.996835,0.996835,0.987421,1,1,0.993711,1,0.962025,1,0.915094,1,0.723101,0.990506,0.996835,1,1,0.465409,0.727848,1,1,0.96519,1,1,0.993711,1,0.993671,0.974684,1,1,1,1,1,1,1,1,1,0.987421,0.987342,1,0.992089,1,0.968553,0.996835]
area_under_roc_curve
0.9870300433882653 [0.920886,1,1,1,1,0.984177,1,1,1,1,0.993671,1,1,1,1,1,1,0.996835,1,0.712025,0.996835,0.963608,1,1,0.993671,1,0.968354,1,1,1,1,1,0.996835,0.993671,0.993711,0.993671,0.996835,0.996835,0.996835,0.996835,0.996835,1,1,0.987421,1,1,1,1,0.996835,0.954114,1,1,0.996835,1,0.718354,0.971519,1,1,0.993671,0.993711,1,0.993671,0.987421,1,1,0.993671,1,1,0.981013,0.990506,0.977848,0.993671,1,1,0.990506,1,1,1,1,1,0.968553,0.996835,1,0.990506,1,1,1,0.993711,1,0.993671,1,1,1,1,0.993671,1,0.996835,0.968553,0.987421,1]
area_under_roc_curve
0.9691787825411987 [1,1,1,1,1,1,1,1,1,1,1,1,0.998418,1,1,0.984177,1,0.993711,0.724684,0.969937,0.981013,1,0.990506,1,1,0.459119,0.449686,0.993671,0.987421,1,1,1,1,1,1,1,0.984177,1,1,1,1,0.996835,1,1,0.993711,0.985759,1,0.993671,1,1,0.465409,1,1,1,0.990506,0.993671,1,0.734177,0.990506,0.45283,1,1,1,0.984177,1,0.993711,0.443396,1,0.995253,0.963608,1,1,1,1,1,1,1,0.981132,1,1,0.996835,1,1,0.996835,1,1,1,0.981132,1,1,1,1,0.993671,1,0.959119,1,0.992089,1,0.719937,1]
area_under_roc_curve
0.9829038044343601 [1,1,1,1,1,0.996835,1,1,0.993711,1,0.993711,1,1,1,1,0.954114,1,0.993711,0.981013,0.993671,0.996835,1,1,1,1,0.977987,0.993711,1,1,0.984177,1,1,1,1,0.996835,1,0.968354,0.950949,1,1,1,1,1,1,1,1,1,1,1,1,0.427673,0.996835,0.962025,0.993711,1,0.976266,1,1,1,1,1,0.72943,1,0.996835,1,0.962264,0.902516,1,0.963608,0.96519,1,1,1,1,0.993671,1,1,0.993711,1,1,0.71519,0.993711,0.987342,1,0.981013,0.962025,1,1,0.993711,1,1,1,1,0.974843,1,1,1,0.996855,0.984177,1]
area_under_roc_curve
0.9753902207228722 [0.993711,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.993671,1,1,1,1,1,1,1,1,0.981132,1,1,0.437107,0.45283,1,1,1,1,1,1,0.993711,1,1,0.996835,0.990506,1,1,0.962264,1,1,1,0.732595,0.723101,1,1,0.943396,1,0.998418,1,1,0.933544,0.988924,0.937107,1,1,1,0.977848,1,1,0.982595,0.993671,1,1,1,1,0.984177,0.993711,1,0.984277,0.734177,1,0.985759,0.996835,1,1,1,1,1,0.993711,1,1,0.993671,1,1,1,0.987421,1,1,0.996835,1,0.992089,0.987421,0.996835,0.993711,0.995253,0.726266,1]
area_under_roc_curve
0.9716514658466681 [0.930818,1,0.474843,1,1,1,1,1,1,1,1,1,0.996835,0.974684,1,1,1,1,0.968354,0.977987,0.990506,1,1,1,1,0.918239,0.981132,0.990506,0.996835,0.987421,1,1,0.987421,0.996855,0.731013,0.993671,0.987342,1,0.732595,1,0.977987,0.993671,1,1,1,1,1,0.993671,0.968553,1,0.984177,0.996835,0.984177,0.724684,1,1,1,1,0.996835,0.987342,1,1,0.738924,0.984177,1,0.993711,1,1,1,1,1,0.993711,0.973101,1,0.981013,0.993671,0.996855,1,1,0.992089,1,0.981132,0.996855,0.487421,0.996835,1,1,1,1,1,1,0.995253,1,1,1,1,1,1,0.727848,1]
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
0.6968022108172127
kappa
0.7283960364770439
kappa
0.646269245953415
kappa
0.6968141802534443
kappa
0.6714708785784798
kappa
0.6716135143669089
kappa
0.7472054350831457
kappa
0.6904122571473701
kappa
0.7473152242577384
kappa
0.6904367053620785
kb_relative_information_score
117.85246736202805
kb_relative_information_score
120.22944261767056
kb_relative_information_score
116.43828305413167
kb_relative_information_score
121.58783219138455
kb_relative_information_score
118.40413747808456
kb_relative_information_score
115.56678900753681
kb_relative_information_score
117.79529836404033
kb_relative_information_score
120.97449283537121
kb_relative_information_score
120.6584074600926
kb_relative_information_score
115.76035598550605
mean_absolute_error
0.011105833333333417
mean_absolute_error
0.011224791666666734
mean_absolute_error
0.011954791666666704
mean_absolute_error
0.011271041666666776
mean_absolute_error
0.011547291666666876
mean_absolute_error
0.012943464486901337
mean_absolute_error
0.011833883928570996
mean_absolute_error
0.011088541666666802
mean_absolute_error
0.011360000000000073
mean_absolute_error
0.01249925595238031
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
0.7
predictive_accuracy
0.73125
predictive_accuracy
0.65
predictive_accuracy
0.7
predictive_accuracy
0.675
predictive_accuracy
0.675
predictive_accuracy
0.75
predictive_accuracy
0.69375
predictive_accuracy
0.75
predictive_accuracy
0.69375
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
0.7 [1,0.5,1,1,1,0,1,1,1,1,0.5,0.5,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0.5,0,1,0.5,0,0.5,1,1,0.5,1,0,1,0,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0.5,1,0,1,0,1,1,0,1,1,1,0.5,1,1,0,0,1,1,0,0,1,0.5,1,1,0.5,1,0,1,1,1,0.5,0,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,1,0,1]
recall
0.73125 [1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,1,0,0,0,1,0,1,1,1,0,0.5,0,1,0,1,1,1,0.5,0.5,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,0,0,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,0,0,1,0.5,0.5,0.5,0,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0.5,1,1,0.5,0,1]
recall
0.65 [0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,0.5,0.5,0.5,1,1,0,0,0,1,1,1,0,0.5,0,1,1,1,1,1,1,0.5,0,0,1,0,1,1,0,1,1,1,0.5,1,0.5,1,0,1,1,1,1,0,1,0.5,1,1,1,0,1,1,0.5,1,0.5,0.5,0,1,0,0,0.5,1,0,1,0,1,1,0.5,0.5,1,1,0.5,0,0.5,1,1,1,0.5,0.5,1,1,1,0,0.5,1,1,0.5,1,0,0.5]
recall
0.7 [0,1,1,1,0.5,0,1,0.5,0.5,0.5,0,0.5,1,0,1,1,1,0,0,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0,1,0.5,1,1,0,0.5,1,1,0.5,1,0.5,1,0,1,1,0.5,0,0.5,0,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,0,1,1,1,0.5]
recall
0.675 [0.5,1,1,1,0,0.5,0,0.5,1,1,0.5,1,0,0,1,1,1,1,1,0,1,0.5,1,1,1,0,0.5,1,0,1,1,1,0.5,0,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0.5,0,0,1,0,0.5,0.5,1,1,0,0.5,1,1,0,1,1,0,1,0.5,0.5,1,0,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,0.5]
recall
0.675 [0,1,1,1,1,0,1,0.5,1,0.5,0,1,1,1,1,1,0.5,1,0,0,0.5,0.5,0.5,1,1,0.5,0.5,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,0,1,0.5,0,1,1,1,1,0.5,1,0.5,1,0,1,1,0,0.5,1,1,0,1,1,0.5,1,1,1,0,1,1,0.5,0,0.5,0.5,1,1,0.5,1,1,1,1,1,0,0.5,1,0.5,0,1,1,0,1,1,1,1,1,1,0.5,1,0.5,0,0,0.5]
recall
0.75 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,0.5,0.5,1,1,1,1,0,0,1,0,1,1,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,0,0.5,0.5,0.5,1,1,0,1,1,1,0.5,1,1,0.5,0.5,0,1,1,1,0.5,1,0,0,1,0.5,0,1,1,1,1,1,1,1,0,0,1,0.5,1,1,0.5,1,1,1,0,0,1,1,1,0.5,1,0,0.5,0.5,1,0,1]
recall
0.69375 [0.5,1,1,1,1,0.5,1,1,1,1,0,0,0.5,0,0,0.5,1,1,0.5,0,0.5,1,1,1,1,0,0,1,0,0.5,1,1,1,1,1,1,0,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,0.5,1,1,0,1,1,1,1,1,0.5,1,0.5,1,0,0,1,0,0.5,0,1,1,1,1,1,1,1,1,1,0,0,0,1,0.5,0.5,1,0,1,1,1,1,1,0,0,1,0.5,0,0,1]
recall
0.75 [0,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,0,0,1,0.5,1,1,1,1,1,1,1,0,0,1,1,0,1,1,1,0.5,0,1,1,0,1,1,1,1,0.5,0.5,0,1,1,1,0.5,1,1,0.5,0,1,1,0,1,0.5,1,1,0,0.5,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,0.5,1,0.5,0,1,0,0.5,0.5,1]
recall
0.69375 [0,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0,0.5,1,1,1,1,0,0,1,0.5,0,1,1,0,0,0.5,0.5,0,1,0.5,1,0,0.5,1,1,1,0.5,1,0.5,0,1,0.5,1,0,0.5,0.5,1,1,1,1,0.5,1,1,0,0.5,1,1,0.5,1,0.5,1,1,0,0,1,0.5,1,1,0,1,0.5,1,0,1,0,0.5,0.5,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,0]
relative_absolute_error
0.5609006734006767
relative_absolute_error
0.5669086700336724
relative_absolute_error
0.6037773569023578
relative_absolute_error
0.5692445286195332
relative_absolute_error
0.5831965488215585
relative_absolute_error
0.6537103276212786
relative_absolute_error
0.5976709054833826
relative_absolute_error
0.5600273569023628
relative_absolute_error
0.5737373737373764
relative_absolute_error
0.6312755531505198
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
0.0686650282976075
root_mean_squared_error
0.0683151754330211
root_mean_squared_error
0.07215429310781667
root_mean_squared_error
0.0689855535400129
root_mean_squared_error
0.07055307300969448
root_mean_squared_error
0.07408553902792571
root_mean_squared_error
0.07057246553624762
root_mean_squared_error
0.0684492994648039
root_mean_squared_error
0.06825493215796899
root_mean_squared_error
0.07341334196414467
root_relative_squared_error
0.6901095002493937
root_relative_squared_error
0.6865933466625327
root_relative_squared_error
0.7251779310665285
root_relative_squared_error
0.6933309001430193
root_relative_squared_error
0.7090850635748697
root_relative_squared_error
0.7445876828692749
root_relative_squared_error
0.7092799658000629
root_relative_squared_error
0.6879413439013002
root_relative_squared_error
0.6859878789671084
root_relative_squared_error
0.7378318481852135
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