6683126
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)
4715460
bootstrap
false
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
14297048
description
https://api.openml.org/data/download/14297048/description.xml
-1
14297049
predictions
https://api.openml.org/data/download/14297049/predictions.arff
-1
14297050
trace
https://api.openml.org/data/download/14297050/trace.arff
area_under_roc_curve
0.9826929450757578 [0.967073,0.999053,1,1,0.999566,0.991694,0.999803,0.995936,0.992858,0.967487,0.997356,0.99931,0.990826,0.956499,0.997376,0.961628,1,0.938901,0.984513,0.940893,0.997652,1,0.995995,1,0.997909,0.865964,0.977391,0.994062,0.987019,0.999487,0.999961,0.999882,0.963936,0.955611,0.999566,0.965534,0.959122,0.982777,0.999211,0.998994,0.992779,0.995245,0.999684,0.992128,0.99929,0.962753,0.9651,0.998067,0.953559,0.961707,0.984592,0.925584,0.988459,0.911103,0.987038,0.942748,1,0.999369,0.99349,0.985934,0.999467,0.996173,0.994062,0.997159,0.996113,0.955769,0.981218,0.999645,0.948844,0.933475,0.954723,0.998895,0.988104,0.996903,0.948509,0.997672,0.999132,0.974294,0.999684,0.998875,0.993134,0.964627,0.99491,0.993056,0.988518,0.999014,0.999842,0.958688,0.994673,0.999842,0.999901,0.998875,0.99491,0.95784,0.997455,0.997416,0.9927,0.987354,0.944898,0.99345]
average_cost
0
f_measure
0.7089016579963584 [0.571429,0.764706,0.969697,1,0.8,0.592593,0.9375,0.8125,0.645161,0.967742,0.75,0.882353,0.647059,0.709677,0.875,0.903226,0.914286,0.387097,0.470588,0.153846,0.764706,0.933333,0.733333,0.969697,0.810811,0.24,0.30303,0.645161,0.592593,0.896552,0.909091,1,0.882353,0.424242,0.810811,0.777778,0.545455,0.529412,0.848485,0.848485,0.645161,0.8125,0.857143,0.6875,0.83871,0.764706,0.875,0.827586,0.555556,0.705882,0.484848,0.606061,0.709677,0.428571,0.551724,0.307692,1,0.848485,0.571429,0.5,0.909091,0.789474,0.666667,0.645161,0.702703,0.411765,0.444444,0.941176,0.551724,0.444444,0.5,0.875,0.571429,0.875,0.666667,0.967742,0.823529,0.428571,0.914286,0.896552,0.588235,0.705882,0.645161,0.705882,0.484848,0.848485,0.969697,0.611111,0.764706,0.969697,0.941176,0.823529,0.62069,0.615385,0.702703,0.875,0.727273,0.571429,0.083333,0.8125]
kappa
0.7133838383838385
kb_relative_information_score
1165.5623762659272
mean_absolute_error
0.012290037587412506
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
0.7115481827683836 [0.666667,0.722222,0.941176,1,0.736842,0.727273,0.9375,0.8125,0.666667,1,0.75,0.833333,0.611111,0.733333,0.875,0.933333,0.842105,0.4,0.444444,0.2,0.722222,1,0.785714,0.941176,0.714286,0.333333,0.294118,0.666667,0.727273,1,0.882353,1,0.833333,0.411765,0.714286,0.7,0.529412,0.5,0.823529,0.823529,0.666667,0.8125,0.789474,0.6875,0.866667,0.722222,0.875,0.923077,0.5,0.666667,0.470588,0.588235,0.733333,0.5,0.615385,0.4,1,0.823529,0.666667,0.583333,0.882353,0.681818,0.714286,0.666667,0.619048,0.388889,0.4,0.888889,0.615385,0.545455,0.583333,0.875,0.526316,0.875,0.818182,1,0.777778,0.5,0.842105,1,0.555556,0.666667,0.666667,0.666667,0.470588,0.823529,0.941176,0.55,0.722222,0.941176,0.888889,0.777778,0.692308,0.8,0.619048,0.875,0.705882,0.526316,0.125,0.8125]
predictive_accuracy
0.71625
prior_entropy
6.6438561897747395
recall
0.71625 [0.5,0.8125,1,1,0.875,0.5,0.9375,0.8125,0.625,0.9375,0.75,0.9375,0.6875,0.6875,0.875,0.875,1,0.375,0.5,0.125,0.8125,0.875,0.6875,1,0.9375,0.1875,0.3125,0.625,0.5,0.8125,0.9375,1,0.9375,0.4375,0.9375,0.875,0.5625,0.5625,0.875,0.875,0.625,0.8125,0.9375,0.6875,0.8125,0.8125,0.875,0.75,0.625,0.75,0.5,0.625,0.6875,0.375,0.5,0.25,1,0.875,0.5,0.4375,0.9375,0.9375,0.625,0.625,0.8125,0.4375,0.5,1,0.5,0.375,0.4375,0.875,0.625,0.875,0.5625,0.9375,0.875,0.375,1,0.8125,0.625,0.75,0.625,0.75,0.5,0.875,1,0.6875,0.8125,1,1,0.875,0.5625,0.5,0.8125,0.875,0.75,0.625,0.0625,0.8125]
relative_absolute_error
0.6207089690612365
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.07167108478439961
root_relative_squared_error
0.7203215046897068
total_cost
0
area_under_roc_curve
0.9785276600191068 [0.987421,1,1,1,1,1,1,1,0.990506,1,0.987342,1,1,1,1,1,1,0.691456,0.97943,0.984277,1,1,0.981132,1,1,0.990506,0.974684,1,1,1,1,1,0.996835,0.987342,1,0.993711,1,1,1,1,1,1,1,0.992089,1,1,1,1,1,0.716772,0.960443,0.996835,1,0.97943,0.987421,0.981132,1,1,0.981132,1,1,1,1,1,1,0.71519,0.988924,1,1,0.937107,0.705696,1,1,1,1,1,0.996835,0.958861,1,1,1,0.996835,0.987421,1,0.981013,0.996835,1,1,0.984177,1,1,1,1,0.971519,1,0.981013,1,0.984177,0.867089,1]
area_under_roc_curve
0.9868795279038294 [0.943396,1,1,1,1,0.993711,1,1,1,1,1,0.996835,0.984177,0.981013,1,1,1,0.969937,0.996835,0.993711,1,1,1,1,1,0.962025,0.993671,1,1,1,1,1,1,0.996835,1,0.993711,0.993711,1,1,1,1,1,1,0.990506,1,1,1,1,0.702532,1,1,0.713608,1,1,0.993711,0.918239,1,1,1,0.939873,1,1,1,0.993671,1,0.987342,1,1,0.924528,0.993711,0.984177,1,1,1,1,0.987342,1,0.993671,1,0.995253,0.996835,0.996835,0.987421,0.949686,0.996835,1,1,0.97943,1,1,1,1,0.996835,0.990506,0.987342,1,1,0.993671,1,1]
area_under_roc_curve
0.9865609575272669 [0.97943,1,1,1,1,0.984177,1,0.984177,0.969937,1,1,1,1,1,0.984177,1,1,0.969937,0.977987,0.686709,0.974843,1,0.995253,1,1,0.974684,0.996835,0.993711,0.996835,1,1,1,1,0.969937,1,1,0.987421,0.95283,1,1,0.982595,1,1,1,1,1,1,1,0.955696,1,0.985759,1,1,0.996835,1,0.988924,1,1,0.987421,0.950949,1,0.993711,0.996835,1,0.996835,0.973101,0.984177,1,0.962264,0.892405,0.957278,1,0.987421,0.996835,1,1,1,0.954114,0.996835,1,1,0.996835,0.973101,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,1,1,0.993671,0.921384,1]
area_under_roc_curve
0.9879665980017511 [0.987342,0.996835,1,1,1,0.946203,1,1,0.987342,1,1,0.996835,1,1,1,1,1,0.987342,0.893082,0.971519,1,1,1,1,1,0.968354,0.981013,1,1,1,1,1,0.996835,0.990506,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.984177,1,0.90566,0.686709,1,0.977848,1,1,1,0.981013,1,1,1,1,0.990506,0.993671,0.901899,1,0.993711,0.968354,0.993671,1,0.993711,1,1,1,1,0.933544,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.993671,1,0.987421,1,0.993671,0.943396,0.97943]
area_under_roc_curve
0.9640570267096571 [1,1,1,1,1,0.993671,1,1,1,1,1,1,0.996855,0.440252,1,1,1,1,1,0.990506,0.993671,1,1,1,1,0.71519,0.974684,1,0.987421,1,1,1,0.732595,0.723101,1,1,0.987342,0.974684,1,1,0.990506,1,1,1,1,1,1,1,1,0.996835,1,0.443396,0.981013,0.937107,0.97943,0.683544,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.984177,0.707278,0.996835,1,1,0.993711,0.718354,1,1,0.962025,1,1,1,1,0.993671,0.988924,0.974843,1,1,1,1,1,1,0.996835,1,0.459119,0.996835,1,0.996835,0.993711,0.91195,1]
area_under_roc_curve
0.982585607236685 [0.939873,1,1,1,1,0.993671,1,0.973101,1,0.743671,0.996835,1,0.930818,1,1,1,1,0.996835,1,0.946203,1,1,0.990506,1,1,0.984177,0.968354,0.993711,1,1,1,1,0.996835,0.987342,1,0.72943,0.987342,0.996835,1,1,0.974684,0.993671,1,1,1,1,1,0.996835,1,0.990506,1,1,0.996835,1,0.993671,0.996835,1,0.996835,0.984177,1,1,1,0.987421,1,1,0.996835,0.993711,1,0.993671,1,0.990506,0.993671,1,1,0.962025,0.993711,0.996835,0.990506,1,1,0.981132,0.734177,1,0.990506,0.993711,1,1,0.993711,0.993671,1,1,1,0.968553,1,0.996835,1,0.974684,0.968553,0.930818,0.969937]
area_under_roc_curve
0.9838119974524322 [1,0.996855,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,0.990506,1,0.987421,1,0.966772,0.996835,1,1,1,0.993671,0.433962,0.924528,0.982595,0.987421,0.996835,1,1,1,0.996855,1,1,1,1,1,1,0.993671,0.974684,1,1,1,0.993671,0.721519,0.995253,0.993711,0.993711,0.981132,1,0.987342,1,0.968354,0.971519,1,1,0.996835,1,0.996835,1,1,0.992089,1,0.981132,1,1,0.996835,0.957278,0.990566,1,0.960443,1,1,1,1,1,1,1,0.987342,1,1,1,0.976266,0.998418,1,0.471698,1,1,1,1,0.981013,0.993711,1,1,1,1,0.977848,0.993671]
area_under_roc_curve
0.9822603196401559 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.71519,1,0.899371,0.996835,0.949367,1,1,1,1,1,0.993711,0.924528,1,0.889937,1,1,1,1,1,1,1,0.721519,0.949367,1,1,1,1,1,0.992089,1,1,1,1,0.993711,1,0.974843,0.996835,0.993671,1,1,0.993671,1,1,1,1,1,0.966772,1,1,1,0.968553,0.974843,1,0.710443,0.966772,0.993711,0.996835,1,0.971698,1,1,1,0.993711,1,1,0.982595,1,1,1,0.996835,1,1,0.993711,0.993711,1,1,1,1,0.993711,1,1,0.984177,0.91195,0.938291,1]
area_under_roc_curve
0.9852335602260964 [0.981132,1,1,1,1,0.993711,1,1,0.996835,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.427673,1,1,1,1,1,1,1,1,1,1,0.996835,0.987342,1,1,0.974843,1,1,0.993671,0.995253,0.71519,1,0.993671,0.981132,1,0.981013,1,1,0.713608,0.973101,0.987421,1,1,0.985759,1,0.996835,1,0.985759,1,1,1,0.993671,1,0.984177,1,1,0.993711,0.974684,1,1,0.996835,1,1,1,1,0.984177,0.981132,1,1,0.996835,1,1,0.993671,1,0.996835,1,0.998418,1,1,1,1,0.993711,0.996835,0.939873,1]
area_under_roc_curve
0.99210577382374 [0.789308,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.974684,1,1,1,0.974843,0.952532,1,0.996835,1,1,1,1,0.839623,1,0.996835,0.974684,1,1,1,1,1,1,0.990506,0.993671,1,1,1,1,0.996835,1,0.96519,1,1,1,0.996835,1,0.993711,0.993671,0.996835,1,0.936709,1,1,1,1,1,1,1,1,0.974684,1,0.993711,1,0.984177,1,0.981013,1,1,1,0.990506,1,0.933544,0.993671,1,1,1,0.996835,1,1,1,0.987421,0.993671,0.996835,1,0.984177,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1]
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
0.7157408504086226
kappa
0.7599115463591849
kappa
0.6778904985591916
kappa
0.7157408504086226
kappa
0.7093666087505923
kappa
0.6842188363464119
kappa
0.6904611497157296
kappa
0.7347228801515869
kappa
0.7283209603538146
kappa
0.7157745144481289
kb_relative_information_score
118.88701812405363
kb_relative_information_score
120.45742804220363
kb_relative_information_score
114.14255289640045
kb_relative_information_score
111.09019987467482
kb_relative_information_score
117.87500027032593
kb_relative_information_score
113.58115301584327
kb_relative_information_score
114.78049737997708
kb_relative_information_score
120.75786471312529
kb_relative_information_score
121.06596814408935
kb_relative_information_score
112.92469380523646
mean_absolute_error
0.011530565476190172
mean_absolute_error
0.01162488095238106
mean_absolute_error
0.012702708333333618
mean_absolute_error
0.013873034604283605
mean_absolute_error
0.011452589285714096
mean_absolute_error
0.012955029761904565
mean_absolute_error
0.012651458333333688
mean_absolute_error
0.011078005952381387
mean_absolute_error
0.01164315476190489
mean_absolute_error
0.01338894841269803
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
0.71875
predictive_accuracy
0.7625
predictive_accuracy
0.68125
predictive_accuracy
0.71875
predictive_accuracy
0.7125
predictive_accuracy
0.6875
predictive_accuracy
0.69375
predictive_accuracy
0.7375
predictive_accuracy
0.73125
predictive_accuracy
0.71875
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
0.71875 [1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0.5,0,1,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,0.5,0.5,1,0,0,0,1,1,0,0.5,1,1,0.5,1,1,0,0.5,1,1,0,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,0.5,0,1]
recall
0.7625 [0,0.5,1,1,1,0,0.5,1,1,1,1,1,0.5,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0,0,0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,1,1,1,1,0.5,0,0.5,1,1,0.5,0.5,0]
recall
0.68125 [0,0.5,1,1,1,0.5,1,0.5,0,1,1,1,1,1,0.5,1,1,0,0,0,0,1,0,1,1,0.5,0.5,0,0.5,1,1,1,1,0.5,1,1,0,0,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,0,1,1,0,0,1,1,0.5,0,0.5,0,0.5,1,0,0,0.5,1,1,1,0,1,1,0,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1]
recall
0.71875 [0.5,1,1,1,0.5,0,1,1,0.5,1,0.5,0.5,0,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,0,1,0.5,1,1,1,0.5,1,1,1,1,0.5,0.5,0,1,0,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0.5,1,0,1,0.5,0,0.5]
recall
0.7125 [0.5,1,1,1,1,0.5,1,1,0,1,1,1,0,0,1,1,1,1,1,0,0.5,1,1,1,1,0,0.5,1,0,0.5,1,1,0.5,0,1,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0.5,1,0.5,0,0,0.5,0,0,0,1,1,1,0,1,1,1,1,0.5,0.5,0,1,0.5,0.5,0.5,1,1,0,0.5,1,1,0,1,1,1,1,0.5,0.5,0,1,1,1,1,1,1,0.5,1,0,1,1,1,1,0,1]
recall
0.6875 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,0,1,1,1,1,0.5,1,0,0.5,0.5,0.5,1,1,0,0,0,0,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,0.5,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0,0,1,0.5,1,0.5,1,1,1,0,0.5,1,1,0,1,1,1,0.5,1,1,1,0,1,0.5,1,0.5,0,0,0.5]
recall
0.69375 [1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0,0,0.5,0,0,1,1,1,0,1,1,0.5,0.5,1,1,0.5,0.5,1,1,0,0.5,0,0.5,1,0,0,1,0,0,0.5,0,1,0.5,0,1,0.5,1,1,0,1,0,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,0,1,0.5,1,0.5,1,1,0,1,1,1,1,0.5,1,1,1,0.5,0,0,1]
recall
0.7375 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,0,0.5,1,0,0.5,0,1,0.5,1,1,0.5,0,0,1,0,1,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,0,0.5,0.5,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,0,0,1,0.5,0,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1]
recall
0.73125 [0,1,1,1,0.5,0,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0,0,1,0.5,1,0.5,0,1,0.5,0,1,0.5,1,1,0.5,0.5,0,1,1,0,0.5,1,1,0.5,0,1,1,0.5,1,0.5,1,1,1,0,1,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0.5,0,0,1,1,0,1,0,1]
recall
0.71875 [0,0.5,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,0,1,1,0,1,0.5,0,1,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0,0.5,1,0,0.5,1,1,1,1,0,1,1,0,1,1,1,0.5,1,0.5,1,0,1,0,1,0,1,1,0,1,0.5,1,0,1,0,1,0.5,1,0.5,1,1,1,1,0.5,0.5,0,1,1,1,0,1]
relative_absolute_error
0.5823517917267753
relative_absolute_error
0.5871151996152041
relative_absolute_error
0.6415509259259393
relative_absolute_error
0.7006583133476556
relative_absolute_error
0.5784136002885898
relative_absolute_error
0.6542944324194214
relative_absolute_error
0.6389625420875589
relative_absolute_error
0.5594952501202711
relative_absolute_error
0.5880381192881248
relative_absolute_error
0.6762095157928286
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
0.06960970593756322
root_mean_squared_error
0.0687304880062415
root_mean_squared_error
0.07372717396289659
root_mean_squared_error
0.07610321352169905
root_mean_squared_error
0.06954141558914925
root_mean_squared_error
0.07411791962290817
root_mean_squared_error
0.07320827878958387
root_mean_squared_error
0.06714960457695793
root_mean_squared_error
0.06908531775663573
root_mean_squared_error
0.0748323486967372
root_relative_squared_error
0.699603867763247
root_relative_squared_error
0.6907673950749239
root_relative_squared_error
0.740985978449054
root_relative_squared_error
0.7648660745205369
root_relative_squared_error
0.6989175239375143
root_relative_squared_error
0.7449131200936547
root_relative_squared_error
0.7357708857357065
root_relative_squared_error
0.6748789187954575
root_relative_squared_error
0.6943335682461759
root_relative_squared_error
0.7520934024488426
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