10088523
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)
8013599
axis
0
8778
copy
true
8778
missing_values
"NaN"
8778
strategy
"most_frequent"
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
"gini"
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
4
8783
min_samples_split
5
8783
min_weight_fraction_leaf
0.0
8783
presort
false
8783
random_state
30056
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
21097583
description
https://api.openml.org/data/download/21097583/description.xml
-1
21097584
predictions
https://api.openml.org/data/download/21097584/predictions.arff
area_under_roc_curve
0.7807112136994949 [0.862532,0.932943,0.873895,0.903902,0.873284,0.83868,0.841106,0.806581,0.746607,0.968415,0.744515,0.839528,0.715159,0.708787,0.965002,0.777107,0.873994,0.615984,0.707722,0.708846,0.775568,0.677754,0.768584,0.936415,0.869022,0.582307,0.645794,0.679431,0.869752,0.779376,0.810507,0.903863,0.776259,0.585938,0.807371,0.840909,0.741675,0.708728,0.839094,0.902403,0.681443,0.840002,0.841422,0.774858,0.682923,0.839863,0.84227,0.871271,0.774148,0.80885,0.773497,0.745324,0.74489,0.647234,0.776476,0.645084,0.999605,0.839863,0.680003,0.61334,0.872001,0.873205,0.745522,0.775765,0.84229,0.586628,0.64753,0.871942,0.647096,0.644709,0.613617,0.842113,0.715692,0.839765,0.740767,0.776949,0.904691,0.677774,0.903981,0.745975,0.676649,0.872455,0.743371,0.80524,0.808239,0.742523,0.873185,0.775331,0.869772,0.777876,0.90406,0.872652,0.743963,0.806483,0.776397,0.682706,0.682331,0.744614,0.640783,0.776772]
average_cost
0
f_measure
0.45967126046377343 [0.360656,0.510638,0.785714,0.685714,0.705882,0.45,0.647059,0.444444,0.451613,0.9375,0.4,0.5,0.4375,0.30303,0.578947,0.516129,0.733333,0.0625,0.243902,0.166667,0.388889,0.266667,0.195122,0.848485,0.444444,0.142857,0.074074,0.235294,0.516129,0.5625,0.62069,0.685714,0.432432,0.105263,0.461538,0.571429,0.27027,0.206897,0.5,0.666667,0.357143,0.571429,0.645161,0.516129,0.086957,0.555556,0.709677,0.666667,0.380952,0.512821,0.388889,0.424242,0.5,0.307692,0.344828,0.133333,0.969697,0.580645,0.27027,0.071429,0.625,0.75,0.451613,0.466667,0.6875,0.133333,0.222222,0.551724,0.285714,0.121212,0.230769,0.6875,0.48,0.62069,0.285714,0.48,0.727273,0.222222,0.727273,0.56,0.210526,0.538462,0.424242,0.315789,0.580645,0.387097,0.75,0.461538,0.592593,0.551724,0.722222,0.689655,0.482759,0.4,0.4,0.461538,0.48,0.5,0.121212,0.470588]
kappa
0.4532828282828283
kb_relative_information_score
827.5737336678114
mean_absolute_error
0.011419880952380834
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
0.4801384579482936 [0.244444,0.387097,0.916667,0.631579,0.666667,0.375,0.611111,0.4,0.466667,0.9375,0.555556,0.583333,0.4375,0.294118,0.5,0.533333,0.785714,0.0625,0.2,0.15,0.35,0.285714,0.16,0.823529,0.4,0.166667,0.090909,0.222222,0.533333,0.5625,0.692308,0.631579,0.380952,0.090909,0.391304,0.666667,0.238095,0.230769,0.583333,0.818182,0.416667,0.666667,0.666667,0.533333,0.142857,0.5,0.733333,0.6,0.307692,0.434783,0.35,0.411765,0.75,0.4,0.384615,0.142857,0.941176,0.6,0.238095,0.083333,0.625,0.75,0.466667,0.5,0.6875,0.142857,0.272727,0.615385,0.333333,0.117647,0.3,0.6875,0.666667,0.692308,0.263158,0.666667,0.705882,0.272727,0.705882,0.777778,0.181818,0.7,0.411765,0.272727,0.6,0.4,0.75,0.6,0.727273,0.615385,0.65,0.769231,0.538462,0.428571,0.428571,0.6,0.666667,0.5,0.117647,0.444444]
predictive_accuracy
0.45875
prior_entropy
6.6438561897747395
recall
0.45875 [0.6875,0.75,0.6875,0.75,0.75,0.5625,0.6875,0.5,0.4375,0.9375,0.3125,0.4375,0.4375,0.3125,0.6875,0.5,0.6875,0.0625,0.3125,0.1875,0.4375,0.25,0.25,0.875,0.5,0.125,0.0625,0.25,0.5,0.5625,0.5625,0.75,0.5,0.125,0.5625,0.5,0.3125,0.1875,0.4375,0.5625,0.3125,0.5,0.625,0.5,0.0625,0.625,0.6875,0.75,0.5,0.625,0.4375,0.4375,0.375,0.25,0.3125,0.125,1,0.5625,0.3125,0.0625,0.625,0.75,0.4375,0.4375,0.6875,0.125,0.1875,0.5,0.25,0.125,0.1875,0.6875,0.375,0.5625,0.3125,0.375,0.75,0.1875,0.75,0.4375,0.25,0.4375,0.4375,0.375,0.5625,0.375,0.75,0.375,0.5,0.5,0.8125,0.625,0.4375,0.375,0.375,0.375,0.375,0.5,0.125,0.5]
relative_absolute_error
0.5767616642616572
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.09286115528571495
root_relative_squared_error
0.933289726308013
total_cost
0
area_under_roc_curve
0.781909655879309 [0.996855,0.992089,0.996835,1,0.496835,1,0.748418,1,0.493671,0.996855,0.490506,0.490506,0.746835,0.742089,1,0.490566,0.493671,0.5,0.993671,0.487421,0.484277,0.981132,0.996855,1,0.987421,0.481013,0.490506,1,0.746835,0.5,0.746835,0.993671,0.726266,0.490506,0.490506,0.490566,0.496855,0.487421,0.996855,0.748418,1,0.996855,0.998418,0.742089,0.496835,0.996835,1,1,1,0.738924,0.474684,0.746835,0.993711,0.487342,1,0.996855,1,1,0.496855,0.740506,1,1,0.748418,0.988924,0.996855,0.496835,0.985759,1,0.996855,0.487421,0.487342,1,0.493671,1,0.993711,1,0.75,0.742089,1,1,0.746835,0.995253,0.996855,0.996855,0.496835,0.735759,0.496835,0.992089,0.998418,0.487421,1,1,0.748418,0.987342,0.75,0.490506,1,1,0.487342,0.990566]
area_under_roc_curve
0.7824831074357138 [1,0.993671,0.75,1,1,0.984277,1,0.5,0.746835,1,0.995253,0.745253,0.746835,0.743671,0.990506,0.996855,0.75,0.737342,0.727848,0.487421,0.996855,0.5,0.977987,1,1,0.493671,0.745253,0.493671,1,0.493711,1,1,0.743671,0.484177,0.990506,1,0.496855,0.987421,1,1,1,1,0.996835,0.740506,0.496835,1,1,1,0.493671,1,1,0.75,0.996855,0.97943,0.493711,0.490566,1,1,0.996855,0.487342,1,0.496855,0.745253,0.735759,0.5,0.496835,0.493671,0.996855,0.487421,0.490566,0.481013,1,0.75,0.748418,0.477987,0.745253,0.746835,0.740506,1,0.490506,0.484177,0.75,0.493711,1,0.742089,0.748418,0.996835,0.493671,0.996835,0.496855,0.75,1,0.745253,0.995253,1,0.496835,0.487421,0.5,0.988924,0.5]
area_under_roc_curve
0.7628856181832656 [0.735759,0.75,1,0.990566,1,0.735759,0.496855,0.746835,0.743671,1,0.75,0.993671,0.993711,0.746835,0.995253,0.992089,0.996835,0.484177,0.493711,0.484177,0.496855,0.481132,0.493671,0.996855,0.990566,0.493671,0.993671,0.484277,0.742089,0.998418,1,1,0.75,0.487342,1,0.496855,1,0.487421,1,0.746835,0.745253,0.487421,1,0.993711,0.745253,0.496855,0.748418,0.996855,0.746835,0.748418,0.990506,0.987421,0.490566,0.5,0.990566,0.735759,1,1,0.490566,0.487342,0.490566,0.996855,0.75,0.493711,1,0.493671,0.996835,0.998418,0.493711,0.724684,0.493671,1,1,0.723101,0.493711,0.75,1,0.487342,0.5,0.75,0.984277,0.990506,0.490506,0.490506,0.996855,0.496855,1,0.748418,0.746835,1,0.746835,1,0.490566,0.742089,0.746835,1,0.496835,0.737342,0.471698,0.742089]
area_under_roc_curve
0.7724176021017434 [0.982595,0.990506,1,1,0.75,0.746835,0.996855,1,0.745253,1,0.742089,0.746835,0.5,0.490506,1,0.742089,1,0.985759,0.490566,0.737342,0.996855,0.487421,0.740506,0.493711,0.490566,0.481013,0.490506,0.493711,0.988924,0.998418,0.993711,0.742089,0.995253,0.740506,1,0.493711,0.993711,0.484277,0.5,0.998418,0.742089,1,0.487421,0.996855,0.746835,0.490566,0.746835,0.996855,0.742089,0.496835,0.496835,0.996855,0.493711,0.493671,0.996855,0.742089,1,0.75,1,0.490506,0.996855,0.5,0.742089,0.490566,0.746835,0.737342,0.490506,0.745253,0.490566,0.732595,0.484177,0.75,0.490566,1,0.493711,0.75,1,0.745253,0.996835,0.75,0.490566,0.75,0.745253,0.987342,1,0.974843,1,0.993671,1,1,1,1,0.484277,0.742089,1,0.496855,0.493671,0.745253,1,0.490506]
area_under_roc_curve
0.7819336637210412 [0.737342,0.981132,0.75,0.998418,1,0.490506,0.5,0.738924,0.496855,1,0.743671,0.740506,0.5,0.481132,0.996855,0.993671,0.746835,0.746835,0.990566,0.75,0.990506,1,0.740506,1,0.996835,0.740506,0.493671,0.496855,0.987421,0.5,0.5,0.5,0.5,0.496835,0.5,0.995253,0.743671,0.743671,0.748418,1,0.742089,1,1,0.493711,0.496855,1,1,0.995253,0.981013,1,0.993711,0.481132,0.998418,0.484277,0.746835,0.732595,0.996855,0.981013,0.737342,0.496855,0.993671,0.996835,0.993711,1,0.746835,0.745253,0.484277,0.745253,0.493671,0.5,0.745253,0.745253,0.996855,0.484277,0.742089,0.990566,0.75,0.746835,0.75,0.996855,0.474843,1,0.996835,0.992089,0.5,0.474843,1,1,0.993671,0.743671,0.977987,0.496835,0.493711,0.996855,0.487342,0.5,0.746835,0.993711,0.484277,0.746835]
area_under_roc_curve
0.7970619626224025 [0.732595,1,1,1,0.996855,1,1,0.723101,1,0.75,0.745253,1,0.5,0.990566,0.981132,0.75,1,0.493671,0.987421,0.740506,0.75,0.75,0.976266,1,0.985759,0.748418,0.710443,0.493711,1,1,1,1,0.746835,0.746835,0.493711,0.75,0.993671,0.982595,0.987342,1,0.742089,1,0.493711,0.996855,0.977987,1,0.748418,0.746835,0.992089,0.748418,0.987421,0.996855,0.5,0.490566,0.738924,0.742089,1,0.745253,0.484177,0.990566,0.996835,0.745253,0.993711,0.496855,1,0.481013,0.493711,1,0.742089,0.5,0.743671,0.496835,1,0.990566,0.742089,0.996855,1,0.493671,0.996835,0.487421,0.484277,0.743671,0.487342,0.990506,0.490566,1,1,1,0.496835,0.496835,1,0.5,0.487421,0.996855,0.737342,1,0.738924,0.496855,0.481132,0.746835]
area_under_roc_curve
0.7953402396306026 [0.992089,0.993711,0.496855,0.748418,1,1,0.990506,0.748418,1,1,1,1,0.995253,0.493711,1,0.493671,1,0.493711,0.742089,0.993671,0.748418,0.484177,0.487342,1,0.735759,0.496855,0.5,0.496835,0.5,0.5,0.745253,0.496855,1,0.490566,0.746835,1,0.974684,0.995253,0.996835,1,0.496835,0.737342,1,0.75,1,0.993671,0.5,0.746835,0.996855,0.996855,0.496855,0.5,1,0.496855,0.993671,0.490506,1,0.746835,0.745253,0.487421,0.740506,1,1,0.737342,1,0.496855,0.490566,0.743671,0.746835,0.748418,0.481132,1,1,1,0.990506,0.481132,0.996855,0.481132,0.993711,0.487421,0.743671,1,0.996835,0.746835,1,0.490506,1,0.496855,0.962264,0.746835,1,1,0.5,0.496855,0.996855,0.496835,0.998418,1,0.734177,1]
area_under_roc_curve
0.7760759991242736 [0.987342,1,1,0.748418,0.496855,0.75,1,0.746835,0.493711,1,0.487421,1,0.745253,0.996855,0.496855,0.748418,1,0.484277,0.487342,0.985759,0.742089,0.742089,0.742089,0.995253,0.993671,0.477987,1,0.987342,0.496855,0.745253,0.75,0.993711,1,0.993711,0.743671,0.746835,0.493671,0.496835,0.746835,1,0.496835,0.996835,1,0.745253,0.493711,0.742089,1,0.745253,0.5,1,0.990566,0.743671,0.487342,0.993711,0.75,0.487342,1,0.996835,0.490506,0.493711,0.746835,1,0.496855,0.996835,0.75,0.490566,0.493711,0.745253,0.734177,0.735759,0.471698,1,0.748418,1,0.742089,1,1,1,1,1,0.490506,0.993711,0.5,0.732595,0.75,0.487342,1,0.481132,1,0.996835,0.496855,0.992089,1,0.496855,1,0.748418,0.748418,0.487421,0.487342,0.746835]
area_under_roc_curve
0.77303148137091 [0.984277,0.996835,1,0.996835,1,0.993711,0.493671,1,0.75,1,0.493711,0.993711,0.493671,0.738924,0.995253,1,1,0.496855,0.5,0.493711,1,0.740506,0.987421,0.75,0.998418,0.487421,0.493711,0.987342,0.996835,1,0.745253,0.996835,0.496855,0.493711,0.995253,0.995253,0.490506,0.493671,0.990506,0.496855,0.487421,0.5,0.493671,0.738924,0.743671,0.746835,1,1,0.490566,0.993711,0.745253,0.748418,0.745253,0.745253,0.496835,0.487421,1,0.490566,0.75,0.735759,0.75,0.75,0.743671,0.745253,0.493711,0.493711,0.490506,1,0.490506,0.962264,1,0.496855,0.5,0.748418,0.490506,0.496835,0.996855,0.487421,1,0.746835,0.990506,1,1,0.493711,1,0.998418,0.996835,0.746835,0.490566,1,0.996835,0.992089,1,0.745253,0.493711,0.740506,0.493711,0.748418,0.716772,1]
area_under_roc_curve
0.7844843414138998 [0.490566,0.745253,0.493711,0.746835,1,0.996855,0.996835,0.984277,0.998418,1,1,1,0.740506,0.740506,1,0.493711,1,0.487421,0.732595,0.493711,0.493671,0.468354,0.987421,1,0.493671,0.984277,0.490566,0.477848,0.984177,1,0.75,1,1,0.496855,0.995253,1,0.743671,0.738924,0.493671,0.977987,0.496855,0.742089,0.748418,0.746835,0.748418,0.745253,1,0.746835,0.490566,0.490566,0.748418,0.748418,0.748418,0.742089,0.748418,0.487421,1,0.493711,0.742089,0.743671,1,1,0.496835,0.746835,0.993711,0.987421,0.743671,0.990566,0.732595,0.493711,0.996855,1,0.493671,0.743671,0.981013,0.745253,0.996855,0.987421,0.996855,0.743671,0.743671,0.496855,0.996855,0.493711,0.993671,1,0.493671,0.745253,1,0.745253,1,1,0.973101,0.743671,0.474843,0.993671,0.493711,0.748418,0.493671,1]
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
0.4633838383838384
kappa
0.4569635739374088
kappa
0.3936762325820077
kappa
0.41882501579279846
kappa
0.4441154407990841
kappa
0.4633203109585257
kappa
0.4819553028508253
kappa
0.4945107021562278
kappa
0.4694667035092566
kappa
0.4442032132001737
kb_relative_information_score
83.9609949587926
kb_relative_information_score
84.01331014536696
kb_relative_information_score
77.12057208937577
kb_relative_information_score
79.6543353655852
kb_relative_information_score
81.42772579769974
kb_relative_information_score
85.75537161488032
kb_relative_information_score
87.80780226673598
kb_relative_information_score
83.4643871884807
kb_relative_information_score
82.02496884697877
kb_relative_information_score
82.34426539392125
mean_absolute_error
0.01110446428571429
mean_absolute_error
0.011317261904761901
mean_absolute_error
0.012005059523809524
mean_absolute_error
0.012017261904761913
mean_absolute_error
0.011934523809523812
mean_absolute_error
0.011303869047619047
mean_absolute_error
0.010765178571428573
mean_absolute_error
0.010876785714285719
mean_absolute_error
0.011236607142857147
mean_absolute_error
0.01163779761904762
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
0.46875
predictive_accuracy
0.4625
predictive_accuracy
0.4
predictive_accuracy
0.425
predictive_accuracy
0.45
predictive_accuracy
0.46875
predictive_accuracy
0.4875
predictive_accuracy
0.5
predictive_accuracy
0.475
predictive_accuracy
0.45
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
0.46875 [1,1,1,1,0,1,0.5,1,0,1,0,0,0.5,0,0.5,0,0,0,0.5,0,0,0,1,1,0,0,0,1,0.5,0,0.5,0.5,0.5,0,0,0,0,0,1,0.5,1,1,0.5,0.5,0,1,1,1,1,0.5,0,0.5,0,0,1,1,1,1,0,0,1,1,0.5,0.5,1,0,0.5,1,0,0,0,1,0,1,0,1,0.5,0,1,1,0.5,1,1,1,0,0.5,0,0.5,0.5,0,1,1,0.5,0.5,0.5,0,1,1,0,1]
recall
0.4625 [1,1,0.5,1,1,0,1,0,0.5,1,0.5,0.5,0.5,0,0.5,1,0.5,0,0.5,0,1,0,0,1,1,0,0,0,1,0,1,1,0.5,0,1,1,0,0,0,1,1,1,1,0,0,1,1,1,0,1,1,0.5,1,0.5,0,0,1,1,1,0,1,0,0.5,0.5,0,0,0,1,0,0,0,1,0,0.5,0,0.5,0,0,1,0,0,0,0,1,0.5,0.5,1,0,0.5,0,0.5,1,0.5,0.5,1,0,0,0,0.5,0]
recall
0.4 [0.5,0.5,1,1,1,0.5,0,0.5,0.5,1,0,0.5,1,0.5,1,0.5,1,0,0,0,0,0,0,1,0,0,0,0,0.5,1,1,1,0,0,1,0,1,0,1,0,0.5,0,1,1,0,0,0.5,1,0,0.5,0.5,0,0,0,0,0,1,1,0,0,0,1,0.5,0,1,0,0.5,0.5,0,0,0,1,1,0,0,0.5,1,0,0,0.5,0,0.5,0,0,1,0,1,0.5,0.5,1,0.5,1,0,0,0.5,1,0,0.5,0,0]
recall
0.425 [1,1,1,1,0.5,0.5,1,1,0.5,1,0,0,0,0,1,0.5,0.5,0,0,0,1,0,0.5,0,0,0,0,0,0.5,1,0,0.5,1,0,1,0,1,0,0,1,0.5,0,0,1,0,0,0.5,1,0.5,0,0,1,0,0,0,0,1,0.5,1,0,1,0,0.5,0,0.5,0,0,0.5,0,0,0,0.5,0,1,0,0.5,1,0.5,1,0.5,0,0,0.5,0,1,0,1,0.5,1,1,1,1,0,0.5,0.5,0,0,0.5,1,0]
recall
0.45 [0.5,0,0.5,0.5,1,0,0,0,0,1,0.5,0,0,0,1,1,0.5,0.5,1,0.5,0.5,1,0.5,1,1,0.5,0,0,0,0,0,0,0,0,0,0.5,0.5,0,0.5,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,0,0.5,0,1,0,0.5,0,0.5,1,0,1,0.5,0.5,0,0,0,0,0.5,0.5,1,0,0.5,0,0.5,0.5,0.5,1,0,1,0.5,0.5,0,0,1,1,0.5,0.5,1,0,0,1,0,0,0.5,1,0,0.5]
recall
0.46875 [0.5,1,0.5,1,1,0.5,1,0,1,0.5,0.5,1,0,1,0,0.5,1,0,0,0,0.5,0.5,0.5,1,0.5,0.5,0,0,1,1,1,1,0.5,0.5,0,0.5,0,0,0,0,0,1,0,1,0,1,0.5,0.5,1,0.5,1,1,0,0,0,0.5,1,0.5,0,1,1,0.5,1,0,1,0,0,1,0.5,0,0.5,0,1,1,0.5,0,1,0,1,0,0,0.5,0,1,0,1,1,1,0,0,1,0,0,1,0,1,0.5,0,0,0.5]
recall
0.4875 [1,0,0,0.5,1,1,1,0.5,1,1,1,1,1,0,1,0,1,0,0.5,0,0.5,0,0,1,0,0,0,0,0,0,0.5,0,1,0,0.5,0.5,0.5,1,1,1,0,0,1,0.5,0,0.5,0,0.5,1,1,0,0,1,0,0.5,0,1,0.5,0,0,0,1,1,0,1,0,0,0.5,0.5,0.5,0,1,1,1,0.5,0,1,0,1,0,0.5,0,1,0.5,1,0,1,0,0,0.5,1,1,0,0,0,0,1,1,0,1]
recall
0.5 [1,1,1,0.5,0,0.5,1,0.5,0,1,0,1,0.5,1,0,0.5,1,0,0,1,0,0,0,1,1,0,1,0.5,0,0.5,0.5,1,1,1,0.5,0.5,0,0,0.5,1,0,1,1,0.5,0,0.5,1,0.5,0,1,1,0.5,0,1,0.5,0,1,1,0,0,0.5,1,0,1,0.5,0,0,0,0.5,0.5,0,1,0.5,1,0.5,1,1,1,0,1,0,0,0,0,0,0,1,0,1,1,0,0.5,1,0,1,0.5,0.5,0,0,0.5]
recall
0.475 [0,1,1,1,1,1,0,1,0.5,1,0,0,0,0.5,0.5,1,1,0,0,0,1,0.5,0,0.5,1,0,0,0.5,1,1,0.5,1,0,0,1,1,0,0,0.5,0,0,0,0,0.5,0,0.5,1,1,0,1,0.5,0.5,0.5,0.5,0,0,1,0,0.5,0,0.5,0.5,0.5,0.5,0,0,0,1,0,0,1,0,0,0.5,0,0,1,0,1,0.5,0.5,1,1,0,1,1,1,0,0,1,1,0.5,1,0.5,0,0.5,0,0.5,0,1]
recall
0.45 [0,0.5,0,0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,0,1,0,0.5,0,0,0,0,1,0,0,0,0,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,0,0,0,0,0.5,0.5,0.5,0.5,1,0.5,0,0,0.5,0.5,0.5,0.5,0.5,0,1,0,0.5,0,1,1,0,0.5,1,1,0.5,0,0.5,0,0,1,0,0,0.5,0,1,0,1,0,0.5,0,1,0,1,0.5,0,0.5,1,0,1,1,0.5,0,0,1,0,0.5,0,1]
relative_absolute_error
0.5608315295815288
relative_absolute_error
0.571578884078883
relative_absolute_error
0.6063161375661366
relative_absolute_error
0.6069324194324188
relative_absolute_error
0.6027537277537269
relative_absolute_error
0.5709024771524761
relative_absolute_error
0.5436958874458866
relative_absolute_error
0.5493326118326112
relative_absolute_error
0.5675054112554104
relative_absolute_error
0.5877675565175556
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
0.09127006426818858
root_mean_squared_error
0.09246657406108494
root_mean_squared_error
0.09619682985263156
root_mean_squared_error
0.0933602708029443
root_mean_squared_error
0.09535148384016653
root_mean_squared_error
0.09163914243544542
root_mean_squared_error
0.09040297005886949
root_mean_squared_error
0.09057716796195978
root_mean_squared_error
0.09308489316533004
root_mean_squared_error
0.09407615977424079
root_relative_squared_error
0.9172986599066806
root_relative_squared_error
0.9293240357885688
root_relative_squared_error
0.966814517099509
root_relative_squared_error
0.938306025997995
root_relative_squared_error
0.9583184700044497
root_relative_squared_error
0.9210080350554777
root_relative_squared_error
0.9085840352091009
root_relative_squared_error
0.910334790008547
root_relative_squared_error
0.935538376605204
root_relative_squared_error
0.9455009808747948
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
159.6730450000905
usercpu_time_millis
143.64908700008527
usercpu_time_millis
147.50107999998363
usercpu_time_millis
107.82221500016931
usercpu_time_millis
107.35240199983309
usercpu_time_millis
106.11552199975449
usercpu_time_millis
106.0374060000413
usercpu_time_millis
105.81872600027964
usercpu_time_millis
103.44104299997525
usercpu_time_millis
104.31516099993132
usercpu_time_millis_testing
2.0477500002016313
usercpu_time_millis_testing
1.992075000089244
usercpu_time_millis_testing
1.922058999980436
usercpu_time_millis_testing
1.5899800000624964
usercpu_time_millis_testing
1.4805569999225554
usercpu_time_millis_testing
1.5135249998365907
usercpu_time_millis_testing
1.569305999964854
usercpu_time_millis_testing
1.493882000204394
usercpu_time_millis_testing
1.47055200000068
usercpu_time_millis_testing
1.4357069999277883
usercpu_time_millis_training
157.62529499988887
usercpu_time_millis_training
141.65701199999603
usercpu_time_millis_training
145.5790210000032
usercpu_time_millis_training
106.23223500010681
usercpu_time_millis_training
105.87184499991054
usercpu_time_millis_training
104.6019969999179
usercpu_time_millis_training
104.46810000007645
usercpu_time_millis_training
104.32484400007525
usercpu_time_millis_training
101.97049099997457
usercpu_time_millis_training
102.87945400000353