10111153
1
Jan van Rijn
9954
Supervised Classification
8815
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1)
8036426
axis
0
8778
copy
true
8778
missing_values
"NaN"
8778
strategy
"median"
8778
verbose
0
8778
copy
true
8779
with_mean
true
8779
with_std
true
8779
memory
null
8780
copy
true
8781
fill_value
-1
8781
missing_values
NaN
8781
strategy
"constant"
8781
verbose
0
8781
categorical_features
null
8782
categories
null
8782
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
8782
handle_unknown
"ignore"
8782
n_values
null
8782
sparse
true
8782
class_weight
null
8783
criterion
"entropy"
8783
max_depth
null
8783
max_features
1.0
8783
max_leaf_nodes
null
8783
min_impurity_decrease
0.0
8783
min_impurity_split
null
8783
min_samples_leaf
11
8783
min_samples_split
10
8783
min_weight_fraction_leaf
0.0
8783
presort
false
8783
random_state
7913
8783
splitter
"best"
8783
n_jobs
null
8812
remainder
"passthrough"
8812
sparse_threshold
0.3
8812
transformer_weights
null
8812
memory
null
8813
memory
null
8815
threshold
0.0
8816
openml-python
Sklearn_0.20.0.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
21142844
description
https://api.openml.org/data/download/21142844/description.xml
-1
21142845
predictions
https://api.openml.org/data/download/21142845/predictions.arff
area_under_roc_curve
0.8386685211489899 [0.829072,0.869792,0.904928,0.897116,0.900825,0.861328,0.80159,0.897293,0.867089,0.837851,0.931897,0.898497,0.833491,0.76233,0.837654,0.823213,0.905086,0.791746,0.794291,0.727884,0.898773,0.83361,0.833787,0.999901,0.801018,0.754399,0.82631,0.835247,0.835346,0.872238,0.987393,0.934738,0.834793,0.817827,0.900272,0.870482,0.795316,0.760989,0.9971,0.965534,0.735519,0.861545,0.871015,0.862926,0.867878,0.764639,0.935823,0.806483,0.727667,0.834497,0.955275,0.824317,0.929096,0.74785,0.76959,0.687441,0.967744,0.92949,0.829013,0.790443,0.77474,0.766927,0.927616,0.640704,0.836233,0.893841,0.788845,0.933495,0.662622,0.724452,0.749112,0.933771,0.80236,0.863991,0.648891,0.930851,0.901002,0.687165,0.900174,0.897865,0.896484,0.767657,0.766907,0.79727,0.857264,0.835938,0.826941,0.854344,0.734612,0.834261,0.965495,0.96437,0.824692,0.786813,0.863557,0.865136,0.861012,0.799025,0.680299,0.793817]
average_cost
0
f_measure
0.4054536772224329 [0.333333,0.628571,0.785714,0.296296,0.5,0.275862,0.322581,0.571429,0.258065,0.428571,0.514286,0.540541,0.457143,0.068966,0.611111,0.214286,0.702703,0.217391,0.4,0.230769,0.545455,0.516129,0.35,0.941176,0.424242,0.2,0.258065,0.5,0.592593,0.685714,0.363636,0.756757,0.470588,0.210526,0.571429,0.727273,0.296296,0.216216,0.740741,0.666667,0.285714,0.333333,0.666667,0.484848,0.645161,0.142857,0.742857,0.484848,0.206897,0.47619,0.465116,0.243902,0.424242,0.16,0.333333,0,0.882353,0.487805,0.193548,0.26087,0.4,0.342857,0.461538,0.266667,0.516129,0.368421,0.068966,0.5625,0.216216,0.111111,0.0625,0.705882,0.378378,0.451613,0.083333,0.529412,0.592593,0.076923,0.666667,0.516129,0.62069,0.307692,0.4,0.133333,0.341463,0.545455,0.24,0.086957,0.2,0.378378,0.666667,0.611111,0.444444,0.173913,0.322581,0.428571,0.424242,0.16,0,0.37037]
kappa
0.41035353535353536
kb_relative_information_score
865.3629284223003
mean_absolute_error
0.013382874893102177
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
0.41230647364057654 [0.357143,0.578947,0.916667,0.363636,0.5,0.307692,0.333333,0.526316,0.266667,0.5,0.473684,0.47619,0.421053,0.076923,0.55,0.25,0.619048,0.166667,0.428571,0.3,0.529412,0.533333,0.291667,0.888889,0.411765,0.214286,0.266667,0.5,0.727273,0.631579,0.352941,0.666667,0.444444,0.181818,0.526316,0.705882,0.363636,0.190476,0.909091,0.6,0.333333,0.269231,0.714286,0.470588,0.666667,0.166667,0.684211,0.470588,0.230769,0.384615,0.37037,0.2,0.411765,0.222222,0.357143,0,0.833333,0.4,0.2,0.428571,0.428571,0.315789,0.391304,0.285714,0.533333,0.318182,0.076923,0.5625,0.190476,0.1,0.0625,0.666667,0.333333,0.466667,0.125,0.5,0.727273,0.1,0.818182,0.533333,0.692308,0.4,0.428571,0.142857,0.28,0.529412,0.333333,0.142857,0.5,0.333333,0.6,0.55,0.545455,0.285714,0.333333,0.346154,0.411765,0.222222,0,0.454545]
predictive_accuracy
0.41625
prior_entropy
6.6438561897747395
recall
0.41625 [0.3125,0.6875,0.6875,0.25,0.5,0.25,0.3125,0.625,0.25,0.375,0.5625,0.625,0.5,0.0625,0.6875,0.1875,0.8125,0.3125,0.375,0.1875,0.5625,0.5,0.4375,1,0.4375,0.1875,0.25,0.5,0.5,0.75,0.375,0.875,0.5,0.25,0.625,0.75,0.25,0.25,0.625,0.75,0.25,0.4375,0.625,0.5,0.625,0.125,0.8125,0.5,0.1875,0.625,0.625,0.3125,0.4375,0.125,0.3125,0,0.9375,0.625,0.1875,0.1875,0.375,0.375,0.5625,0.25,0.5,0.4375,0.0625,0.5625,0.25,0.125,0.0625,0.75,0.4375,0.4375,0.0625,0.5625,0.5,0.0625,0.5625,0.5,0.5625,0.25,0.375,0.125,0.4375,0.5625,0.1875,0.0625,0.125,0.4375,0.75,0.6875,0.375,0.125,0.3125,0.5625,0.4375,0.125,0,0.3125]
relative_absolute_error
0.6759027723788965
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08940557042458111
root_relative_squared_error
0.8985597917152455
total_cost
0
area_under_roc_curve
0.8443401202133586 [0.990566,0.995253,1,0.987421,0.993671,0.993711,0.743671,0.996855,0.996835,1,0.996835,0.738924,0.732595,0.724684,1,0.990566,0.996835,0.484177,0.487342,0.477987,0.996855,0.481132,0.987421,1,0.984277,0.738924,0.742089,0.990506,0.742089,0.5,0.990506,1,0.745253,0.734177,1,0.487421,0.974843,0.484277,1,1,1,0.984277,0.75,0.993671,0.996835,0.990506,1,1,0.468354,0.737342,0.738924,0.737342,0.981132,0.721519,0.993711,0.987421,0.996835,1,0.968553,0.731013,0.490566,0.996855,0.745253,0.477848,1,0.993671,0.981013,1,0.455975,0.949686,0.672468,1,0.743671,0.737342,0.990566,0.987342,0.996835,0.982595,0.746835,0.995253,1,0.477848,0.987421,0.996855,0.732595,0.738924,0.958861,0.72943,1,0.474843,0.987342,1,0.996835,0.963608,0.992089,0.735759,0.993711,0.493671,0.452532,0.996855]
area_under_roc_curve
0.8537556723190827 [1,1,0.748418,1,0.990506,1,0.735759,0.996855,0.743671,1,0.995253,0.743671,0.743671,0.742089,0.996835,0.996855,1,0.723101,0.735759,0.993711,1,0.971698,0.984277,1,0.996855,0.713608,0.976266,0.493671,0.993671,1,0.998418,1,0.984177,0.734177,0.974684,1,0.471698,0.990566,0.993711,0.996835,0.490566,0.996855,1,0.995253,0.742089,0.731013,0.996855,0.993711,0.471519,0.990506,0.911392,0.726266,0.996855,0.968354,1,0.471698,1,0.996855,0.990566,0.716772,1,0.993711,0.996835,0.496835,1,0.993671,0.735759,0.981132,0.455975,0.955975,0.949367,1,0.742089,0.993671,0.946541,0.996835,0.977848,0.723101,0.998418,0.75,0.985759,0.735759,0.459119,0.455975,0.974684,0.704114,0.487342,0.950949,0.740506,0.490566,0.746835,0.996855,0.957278,0.718354,0.973101,0.987342,0.996855,0.732595,0.455696,0.493711]
area_under_roc_curve
0.8159379726932572 [0.737342,0.743671,0.976266,1,0.743671,0.96519,0.493711,1,0.990506,0.732595,0.731013,0.990506,1,0.5,0.481013,0.731013,0.996835,0.716772,0.474843,0.481013,1,0.984277,0.719937,0.996855,0.481132,0.996835,0.990506,0.996855,1,0.996835,0.968553,0.990506,0.996835,0.719937,0.487421,0.996855,0.996855,0.477987,1,1,0.490506,1,1,0.996855,0.727848,0.459119,0.746835,0.996855,0.731013,0.746835,0.993671,0.996855,0.977987,0.474684,0.996855,0.974684,1,0.988924,0.987421,0.471519,0.496855,0.493711,0.995253,0.996855,0.982595,0.737342,0.746835,0.996835,0.481132,0.471519,0.974684,0.993671,0.484277,0.742089,0.459119,0.97943,1,0.468354,0.734177,0.996835,0.471698,0.742089,0.996835,0.737342,0.996855,0.996855,1,0.992089,0.746835,0.996855,0.990506,0.996855,0.477987,0.737342,0.738924,0.993711,0.484177,0.985759,0.477987,0.734177]
area_under_roc_curve
0.8536636215269484 [0.738924,0.998418,1,0.996855,0.743671,0.487342,0.993711,0.97943,0.996835,0.743671,0.996835,0.992089,0.493711,0.982595,0.996835,0.988924,0.743671,0.968354,0.462264,0.455696,0.962264,1,0.746835,1,0.490566,0.985759,0.737342,0.990566,0.985759,0.746835,0.993711,0.996835,0.742089,0.974684,0.996855,0.496855,0.996855,0.924528,0.996855,1,0.735759,0.977987,1,0.971698,0.996835,0.996855,0.998418,1,0.968354,0.990506,0.993671,0.987421,0.474843,0.455696,0.996855,0.96519,1,0.995253,0.993711,0.985759,0.990566,0.981132,0.971519,0.474843,0.746835,0.987342,0.731013,1,0.987421,0.732595,0.705696,0.738924,0.993711,0.973101,0.446541,0.746835,0.746835,0.474684,1,0.987342,0.487421,0.974684,1,0.696203,0.930818,0.496855,0.996855,0.993671,0.458861,0.984277,1,0.996855,0.477987,0.468354,0.990506,0.496855,0.740506,0.973101,0.946541,0.697785]
area_under_roc_curve
0.8089590299339221 [0.740506,1,0.75,1,0.490566,0.743671,0.487421,0.738924,0.487421,0.746835,0.981013,0.993671,0.996855,0.468553,0.993711,0.982595,0.75,0.993671,0.996855,0.993671,0.490506,0.718354,1,0.996835,0.740506,0.710443,0.973101,1,1,0.745253,0.990566,1,0.490506,0.455696,0.981132,1,0.72943,0.487342,0.990506,0.987342,0.992089,0.742089,0.5,0.993711,0.996855,0.484277,1,0.496835,0.746835,0.984177,0.974843,0.477987,0.998418,0.95283,0.740506,0.455696,1,0.731013,0.988924,0.484277,0.748418,0.743671,0.993711,0.493711,0.740506,0.949367,0.474843,0.987342,0.477848,0.712025,0.738924,0.996835,0.993711,0.490566,0.96519,1,0.746835,0.696203,0.992089,0.987421,0.465409,0.996835,0.477848,0.740506,0.993711,1,0.487421,1,1,0.735759,0.996855,0.987342,0.959119,0.968553,0.748418,0.990566,0.735759,0.993711,0.468553,0.734177]
area_under_roc_curve
0.8347504179603534 [0.719937,1,1,0.984177,0.993711,0.993671,0.996855,0.742089,0.993711,0.742089,0.990506,1,0.993711,0.471698,1,0.737342,1,0.987342,0.955975,0.727848,1,0.740506,0.75,1,0.734177,0.724684,0.726266,1,1,1,0.996855,1,0.735759,0.981013,0.490566,1,0.732595,0.72943,1,0.996835,0.996835,0.732595,1,0.996855,0.996855,0.984277,1,0.746835,0.727848,0.738924,0.981132,0.968553,0.990506,0.993711,0.477848,0.471519,0.993711,0.996835,0.993671,0.993711,0.745253,0.727848,0.490566,0.993711,0.740506,0.743671,0.468553,0.996835,0.727848,1,0.468354,0.993671,0.949686,0.996855,0.455696,1,0.745253,0.716772,0.996835,0.490566,0.996855,0.490506,0.731013,0.726266,0.484277,0.487421,0.971698,0.471698,0.477848,0.990506,0.996855,0.990506,0.487421,0.981132,0.988924,0.996855,0.982595,0.484277,0.959119,0.738924]
area_under_roc_curve
0.8301038432449642 [0.987342,0.484277,1,0.710443,1,0.993671,0.984177,0.996835,0.990566,0.735759,1,0.996855,0.72943,0.981132,0.490566,0.716772,0.493711,0.477987,0.712025,0.97943,0.738924,0.742089,0.738924,1,0.731013,0.471698,0.981132,0.742089,0.481132,1,0.995253,0.493711,0.996855,0.993711,1,0.75,0.990506,0.738924,0.998418,0.493711,0.484177,0.987342,0.740506,0.984177,0.996855,0.971519,0.746835,0.740506,0.971698,0.987421,1,0.987342,0.993671,0.468553,0.731013,0.721519,1,0.743671,0.984177,0.915094,0.987342,0.471519,0.987421,0.481013,0.735759,0.981132,0.468553,0.998418,0.727848,0.468354,0.990566,1,0.732595,0.993711,0.446203,0.984277,1,0.965409,1,0.996855,0.995253,0.990566,0.740506,0.987342,0.993671,0.993671,0.732595,0.474843,1,0.75,0.996855,0.996835,0.735759,0.987421,0.493711,0.735759,0.984177,0.496855,0.712025,0.985759]
area_under_roc_curve
0.8172705944988454 [0.996835,0.993711,1,0.477848,1,0.716772,0.993671,0.996835,0.493711,0.996835,0.993711,0.487421,0.732595,0.981132,0.481132,0.468354,1,0.465409,0.971519,0.481013,0.977848,0.737342,0.732595,1,0.990506,0.440252,0.45283,0.738924,1,0.742089,0.981013,0.996855,1,1,0.740506,1,0.737342,0.984177,0.993671,0.996855,0.735759,0.735759,0.993671,0.490506,1,0.735759,1,1,0.962264,0.493711,0.974843,0.723101,0.737342,1,0.746835,0.723101,1,0.988924,0.484177,0.996855,0.493671,0.737342,0.984277,0.738924,0.746835,0.984277,0.993711,0.743671,0.481013,0.732595,0.465409,1,0.996835,0.468553,0.721519,1,0.977987,0.940252,1,0.962264,0.968354,1,0.732595,0.993671,0.471519,0.738924,0.990506,0.481132,1,0.737342,1,0.996835,0.72943,0.965409,0.474843,0.987342,0.984177,0.990566,0.697785,0.727848]
area_under_roc_curve
0.8615977778441211 [0.471698,0.995253,1,1,0.996835,0.990566,0.732595,0.955975,0.974684,1,0.490566,0.996855,0.996835,0.745253,0.996835,0.968553,0.993711,0.993711,1,0.949686,0.993671,1,0.987421,1,0.748418,0.981132,0.465409,0.990506,0.742089,0.996855,0.990506,0.996835,0.949686,0.937107,0.996835,1,0.740506,0.721519,0.993671,0.996855,0.487421,0.727848,0.746835,0.735759,0.487342,0.493671,0.990566,0.97943,0.465409,0.996855,0.987342,0.982595,0.993671,0.705696,0.740506,0.471698,0.75,0.981132,0.742089,0.710443,0.745253,0.993671,0.988924,0.735759,1,0.987421,0.97943,0.5,0.732595,0.987421,0.984277,0.984277,0.742089,0.993671,0.455696,0.732595,0.996855,0.471698,1,1,1,0.990566,0.477987,0.987421,0.976266,1,0.958861,0.977848,0.471698,0.987342,1,0.746835,0.996835,0.707278,0.993711,0.726266,0.955975,0.727848,0.732595,1]
area_under_roc_curve
0.863410805270281 [0.962264,0.484177,0.496855,0.966772,1,0.987421,0.737342,0.484277,0.735759,0.993711,1,0.993711,0.993671,0.987342,0.742089,1,1,1,0.993671,0.996855,1,0.996835,1,1,1,0.459119,0.996855,0.743671,0.484177,0.996855,0.985759,0.75,1,0.946541,0.992089,0.732595,0.726266,0.996835,1,1,0.959119,0.981013,0.998418,0.743671,1,0.738924,1,0.471519,0.993711,0.490566,0.995253,0.732595,0.996835,0.955696,0.738924,0.45283,1,0.987421,0.471519,1,0.993671,0.731013,0.993671,0.724684,0.990566,0.5,0.94462,0.993711,0.985759,0.462264,0.471698,0.490566,0.737342,0.996835,0.734177,0.995253,1,0.474843,0.477987,0.743671,1,0.493711,0.996855,0.484277,0.987342,1,0.72943,0.969937,0.455975,0.990506,1,0.996835,0.996835,0.737342,1,1,1,0.996835,0.976266,0.984277]
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
0.44415933046464806
kappa
0.4379317150187487
kappa
0.40003947108742843
kappa
0.40630797773654914
kappa
0.4124615020137409
kappa
0.4002998500749625
kappa
0.34954215345753076
kappa
0.35593354118157783
kappa
0.4001815240124699
kappa
0.4947102479077846
kb_relative_information_score
89.77149562201704
kb_relative_information_score
90.1624253792219
kb_relative_information_score
82.81890028795964
kb_relative_information_score
87.51139141986737
kb_relative_information_score
80.24493357077337
kb_relative_information_score
86.09231858690346
kb_relative_information_score
82.96544121447403
kb_relative_information_score
80.46088414880147
kb_relative_information_score
89.92012874213064
kb_relative_information_score
95.41500945015949
mean_absolute_error
0.012833338202475206
mean_absolute_error
0.012949762941844018
mean_absolute_error
0.013452081632785001
mean_absolute_error
0.013511929535033254
mean_absolute_error
0.013703870554862243
mean_absolute_error
0.01345262408246638
mean_absolute_error
0.014090985156484387
mean_absolute_error
0.013789927364946719
mean_absolute_error
0.013501989062171909
mean_absolute_error
0.01254224039795344
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
0.45
predictive_accuracy
0.44375
predictive_accuracy
0.40625
predictive_accuracy
0.4125
predictive_accuracy
0.41875
predictive_accuracy
0.40625
predictive_accuracy
0.35625
predictive_accuracy
0.3625
predictive_accuracy
0.40625
predictive_accuracy
0.5
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
0.45 [0,0.5,1,0,0.5,1,0.5,1,0,0,1,0.5,0,0,1,1,1,0,0,0,1,0,1,1,0,0.5,0.5,0.5,0.5,0,1,1,0.5,0,1,0,0,0,1,1,1,0,0.5,1,1,0.5,1,1,0,0.5,0.5,0.5,0,0,1,0,1,1,0,0,0,1,0,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,0.5,0.5,0,0.5,0.5,1,0,0,1,0.5,0.5,0,0,0,0,1,1,1,0,0.5,0.5,0,0,0,1]
recall
0.44375 [1,1,0.5,1,0,1,0.5,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,0,0.5,1,1,0,0,1,1,0,0,0,0.5,1,0.5,1,0.5,0.5,0,1,0,0,0,1,0,1,1,0.5,0.5,0,1,0,0,1,0,0.5,1,0.5,1,0,1,1,0,0,1,1,1,0,1,1,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,0,0,0,0.5,0,0,0,0,0,0.5,1,0,0,0.5,0.5,1,0,0,0]
recall
0.40625 [0,0.5,0,1,0.5,0,0,1,0.5,0,0.5,0.5,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,1,1,0,1,1,0,0,1,0,0,1,1,0,1,1,1,0,0,0.5,1,0.5,0.5,1,1,0,0,1,0,1,0.5,0,0,0,0,0.5,1,0.5,0.5,0,1,0,0,0,1,0,0.5,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0.5,1,0,0.5,0.5,1,0,0.5,0,0.5]
recall
0.4125 [0.5,1,1,0,0.5,0,1,0.5,0,0,1,0.5,0,0,1,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0,0,0.5,1,1,0.5,0.5,1,0,1,0,1,1,0,0,1,0,0.5,1,0.5,1,0,1,1,1,0,0,1,0,1,1,0,0,1,0,1,0,0.5,1,0,1,0,0.5,0,0,1,0.5,0,0.5,0,0,1,0.5,0,0.5,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0.5,0,0,0]
recall
0.41875 [0.5,1,0.5,1,0,0.5,0,0,0,0.5,0,0.5,0,0,1,0.5,0.5,1,1,1,0,0,1,1,0.5,0,0,1,1,0.5,1,1,0,0,1,1,0.5,0,0.5,1,1,0.5,0,1,1,0,1,0,0.5,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0.5,0.5,0,0,0.5,0,0,0,0,0,0,1,1,0,0,1,0.5,0,0.5,0,0,1,0,0.5,0,1,0,1,1,0.5,0,1,0,0,0.5,0,0.5,1,0,0]
recall
0.40625 [0,1,1,0,1,0,0,0.5,1,0.5,0.5,1,1,0,1,0,1,0.5,0,0,1,0.5,0,1,0.5,0,0.5,1,1,1,0,1,0,0,0,1,0,0.5,1,0,0.5,0.5,1,1,1,0,1,0.5,0,0.5,1,0,0,0,0,0,1,1,1,0,0.5,0,0,1,0,0.5,0,1,0.5,0.5,0,1,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0.5,0,0,0.5]
recall
0.35625 [0.5,0,1,0,1,0,0,1,0,0,1,1,0.5,0,0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0.5,0,1,0.5,0,1,1,1,0.5,0,0.5,0.5,0,0,0,0.5,0,1,0,0.5,0.5,0,1,1,0,0.5,0,0,0,1,0.5,0,0,0,0,1,0,0,0,0,0.5,0.5,0,0,1,0,0,0,1,1,0,1,1,0.5,0,0.5,0,1,1,0,0,0,0.5,1,1,0.5,0,0,0.5,0.5,0,0,0.5]
recall
0.3625 [0.5,1,1,0,1,0,0.5,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0.5,0,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0.5,0,0.5,1,0,0.5,1,0,1,0,1,1,0,0,0,0,0.5,1,0,0,1,0.5,0,1,0,0.5,0,0.5,0.5,0,0,0,0,0,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0.5,0,1,0,0,0,1,0.5,0,0,0]
recall
0.40625 [0,1,1,0,0,1,0.5,0,0,1,0,1,1,0,1,0,1,0,1,0,0.5,1,1,1,0.5,0,0,0.5,0.5,1,0,1,0,0,1,1,0.5,0,0,0,0,0.5,0,0.5,0,0,1,0.5,0,1,0.5,0.5,1,0,0,0,0.5,0,0,0,0,1,0.5,0.5,1,0,0,0,0.5,0,1,0,0.5,0,0,0.5,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,0,1,0,0.5,0,1,0.5,0,0,0,1]
recall
0.5 [0,0,0,0,1,0,0,0,0.5,0,1,1,1,0,0.5,1,1,1,1,0,1,1,1,1,1,0,1,0.5,0,1,0,0.5,1,0,0.5,0.5,0,1,1,1,0,0.5,0.5,0.5,1,0,1,0,1,0,1,0,1,0,0,0,1,1,0,1,1,0,1,0,1,0,0,1,0.5,0,0,0,0,1,0.5,0.5,1,0,0,0.5,1,0,1,0,0.5,1,0,0,0,0.5,1,1,1,0.5,0,1,1,0,0,0]
relative_absolute_error
0.6481483940644033
relative_absolute_error
0.6540284314062624
relative_absolute_error
0.6793980622618676
relative_absolute_error
0.6824206835875369
relative_absolute_error
0.692114674487991
relative_absolute_error
0.6794254587104221
relative_absolute_error
0.7116659169941597
relative_absolute_error
0.6964609780276109
relative_absolute_error
0.6819186395036305
relative_absolute_error
0.6334464847451222
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
0.08711950530460409
root_mean_squared_error
0.08660146021397032
root_mean_squared_error
0.09196972716135479
root_mean_squared_error
0.0891680601602747
root_mean_squared_error
0.09172444527430204
root_mean_squared_error
0.08990751006324724
root_mean_squared_error
0.09236885888622237
root_mean_squared_error
0.09129114699200075
root_mean_squared_error
0.08903085574423
root_mean_squared_error
0.08452962992109363
root_relative_squared_error
0.8755839727780258
root_relative_squared_error
0.8703774237170626
root_relative_squared_error
0.9243305365623377
root_relative_squared_error
0.8961727237438442
root_relative_squared_error
0.9218653608434764
root_relative_squared_error
0.9036044748936156
root_relative_squared_error
0.9283419613299532
root_relative_squared_error
0.9175105492534797
root_relative_squared_error
0.8947937674783895
root_relative_squared_error
0.849554745805656
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
166.3528689987288
usercpu_time_millis
151.44001800035767
usercpu_time_millis
147.96078199924523
usercpu_time_millis
111.17120600010821
usercpu_time_millis
112.92195000078209
usercpu_time_millis
113.8778500007902
usercpu_time_millis
112.87329900005716
usercpu_time_millis
116.3152720000653
usercpu_time_millis
113.78908000096999
usercpu_time_millis
112.84121800053981
usercpu_time_millis_testing
1.8056189992421423
usercpu_time_millis_testing
1.8047410003418918
usercpu_time_millis_testing
1.7540290000397363
usercpu_time_millis_testing
1.4057970001886133
usercpu_time_millis_testing
1.5626530002919026
usercpu_time_millis_testing
1.623064000341401
usercpu_time_millis_testing
1.3697089998458978
usercpu_time_millis_testing
1.4346580001074472
usercpu_time_millis_testing
1.2619960007214104
usercpu_time_millis_testing
1.275041000553756
usercpu_time_millis_training
164.54724999948667
usercpu_time_millis_training
149.63527700001578
usercpu_time_millis_training
146.2067529992055
usercpu_time_millis_training
109.7654089999196
usercpu_time_millis_training
111.35929700049019
usercpu_time_millis_training
112.2547860004488
usercpu_time_millis_training
111.50359000021126
usercpu_time_millis_training
114.88061399995786
usercpu_time_millis_training
112.52708400024858
usercpu_time_millis_training
111.56617699998606