10110777
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)
8036046
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
"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
10
8783
min_samples_split
11
8783
min_weight_fraction_leaf
0.0
8783
presort
false
8783
random_state
49784
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
21142092
description
https://api.openml.org/data/download/21142092/description.xml
-1
21142093
predictions
https://api.openml.org/data/download/21142093/predictions.arff
area_under_roc_curve
0.8352485795454545 [0.83142,0.869969,0.905125,0.931404,0.901535,0.830749,0.769788,0.867582,0.867681,0.808712,0.931029,0.900114,0.835997,0.762449,0.83791,0.792732,0.905481,0.793935,0.796599,0.732876,0.8997,0.865964,0.834517,0.999961,0.834852,0.725379,0.76525,0.83574,0.836076,0.871843,0.960582,0.904179,0.804905,0.821634,0.900844,0.870995,0.793896,0.76164,0.995699,0.965396,0.801255,0.833037,0.871745,0.862512,0.86918,0.800742,0.935172,0.806917,0.727884,0.83724,0.956124,0.795474,0.930634,0.78123,0.772905,0.689789,0.998146,0.930181,0.796934,0.826073,0.774621,0.79796,0.928958,0.641828,0.837279,0.833886,0.791351,0.935646,0.661478,0.728555,0.756866,0.933692,0.803405,0.867345,0.649562,0.931088,0.900548,0.691742,0.902285,0.898497,0.894551,0.703934,0.766473,0.765803,0.859947,0.83578,0.828283,0.792337,0.735894,0.804076,0.965534,0.964824,0.827849,0.758108,0.865175,0.86626,0.893663,0.735361,0.652442,0.796658]
average_cost
0
f_measure
0.42356028732045303 [0.333333,0.628571,0.785714,0.380952,0.571429,0.266667,0.285714,0.588235,0.322581,0.413793,0.526316,0.611111,0.470588,0.066667,0.625,0.148148,0.702703,0.232558,0.352941,0.451613,0.5,0.5625,0.358974,0.969697,0.484848,0.2,0.26087,0.545455,0.571429,0.666667,0.5,0.722222,0.5,0.205128,0.55,0.727273,0.357143,0.25,0.722222,0.666667,0.413793,0.421053,0.709677,0.457143,0.6875,0.266667,0.714286,0.514286,0.206897,0.571429,0.44898,0.27027,0.457143,0.166667,0.344828,0,0.864865,0.512821,0.2,0.296296,0.413793,0.363636,0.45,0.266667,0.592593,0.45,0.129032,0.666667,0.216216,0.1875,0.066667,0.6875,0.4,0.5625,0.076923,0.5,0.571429,0.074074,0.740741,0.533333,0.571429,0.222222,0.363636,0.171429,0.421053,0.482759,0.307692,0.071429,0.285714,0.4,0.6,0.611111,0.384615,0.214286,0.296296,0.45,0.424242,0.222222,0,0.296296]
kappa
0.42803030303030304
kb_relative_information_score
875.1010045306258
mean_absolute_error
0.012986604423805387
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
0.432821536265119 [0.357143,0.578947,0.916667,0.8,0.526316,0.285714,0.333333,0.555556,0.333333,0.461538,0.454545,0.55,0.444444,0.071429,0.625,0.181818,0.619048,0.185185,0.333333,0.466667,0.5,0.5625,0.304348,0.941176,0.470588,0.214286,0.428571,0.529412,0.666667,0.6,0.416667,0.65,0.45,0.173913,0.458333,0.705882,0.416667,0.25,0.65,0.647059,0.461538,0.363636,0.733333,0.421053,0.6875,0.285714,0.833333,0.473684,0.230769,0.526316,0.333333,0.238095,0.421053,0.25,0.384615,0,0.761905,0.434783,0.214286,0.363636,0.461538,0.352941,0.375,0.285714,0.727273,0.375,0.133333,0.647059,0.190476,0.1875,0.071429,0.6875,0.368421,0.5625,0.1,0.45,0.666667,0.090909,0.909091,0.571429,0.666667,0.272727,0.352941,0.157895,0.363636,0.538462,0.4,0.083333,0.6,0.368421,0.642857,0.55,0.5,0.25,0.363636,0.375,0.411765,0.272727,0,0.363636]
predictive_accuracy
0.43375
prior_entropy
6.6438561897747395
recall
0.43375 [0.3125,0.6875,0.6875,0.25,0.625,0.25,0.25,0.625,0.3125,0.375,0.625,0.6875,0.5,0.0625,0.625,0.125,0.8125,0.3125,0.375,0.4375,0.5,0.5625,0.4375,1,0.5,0.1875,0.1875,0.5625,0.5,0.75,0.625,0.8125,0.5625,0.25,0.6875,0.75,0.3125,0.25,0.8125,0.6875,0.375,0.5,0.6875,0.5,0.6875,0.25,0.625,0.5625,0.1875,0.625,0.6875,0.3125,0.5,0.125,0.3125,0,1,0.625,0.1875,0.25,0.375,0.375,0.5625,0.25,0.5,0.5625,0.125,0.6875,0.25,0.1875,0.0625,0.6875,0.4375,0.5625,0.0625,0.5625,0.5,0.0625,0.625,0.5,0.5,0.1875,0.375,0.1875,0.5,0.4375,0.25,0.0625,0.1875,0.4375,0.5625,0.6875,0.3125,0.1875,0.25,0.5625,0.4375,0.1875,0,0.25]
relative_absolute_error
0.6558891123134022
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08885842016206247
root_relative_squared_error
0.8930607246706431
total_cost
0
area_under_roc_curve
0.8484916109386192 [0.990566,0.992089,1,0.987421,0.993671,0.993711,0.743671,1,0.996835,1,0.996835,0.738924,0.738924,0.72943,1,0.990566,0.996835,0.484177,0.487342,0.477987,0.996855,0.481132,0.981132,1,0.990566,0.738924,0.742089,0.990506,0.742089,0.5,0.977848,1,0.745253,0.734177,1,0.487421,0.974843,0.484277,1,1,1,0.984277,1,0.996835,0.996835,0.990506,1,1,0.468354,0.734177,0.745253,0.740506,0.984277,0.726266,0.993711,0.987421,0.996835,1,0.968553,0.732595,0.490566,0.996855,0.740506,0.481013,1,0.993671,0.981013,1,0.468553,0.981132,0.707278,1,0.743671,0.737342,0.990566,0.992089,0.996835,0.982595,0.746835,0.988924,1,0.484177,0.987421,0.996855,0.735759,0.738924,0.96519,0.72943,1,0.474843,0.984177,1,0.996835,0.963608,0.992089,0.737342,0.993711,0.493671,0.452532,0.996855]
area_under_roc_curve
0.8442810335562452 [1,1,0.748418,1,0.993671,1,0.735759,0.996855,0.743671,1,0.998418,0.996835,0.743671,0.742089,0.996835,0.996855,1,0.735759,0.735759,0.993711,1,0.977987,0.984277,1,0.996855,0.718354,0.732595,0.493671,0.996835,1,0.998418,1,0.984177,0.737342,0.993671,1,0.477987,0.984277,0.987421,1,0.490566,1,1,0.996835,0.742089,0.745253,0.993711,0.993711,0.468354,1,0.962025,0.726266,0.993711,0.977848,1,0.477987,1,0.996855,0.493711,0.719937,1,0.993711,0.996835,0.496835,1,0.993671,0.740506,0.987421,0.462264,0.981132,0.949367,1,0.742089,1,0.896226,0.996835,0.963608,0.72943,0.998418,0.75,0.985759,0.735759,0.468553,0.462264,0.732595,0.723101,0.490506,0.696203,0.745253,0.496855,0.746835,0.996855,0.981013,0.493671,0.966772,0.990506,1,0.732595,0.462025,0.493711]
area_under_roc_curve
0.8078076486744686 [0.738924,0.743671,0.987342,1,0.738924,0.724684,0.493711,1,0.992089,0.490506,0.72943,0.748418,1,0.5,0.481013,0.732595,0.996835,0.716772,0.474843,0.481013,1,0.974843,0.704114,1,0.490566,0.996835,0.990506,0.996855,1,0.996835,0.968553,0.990506,0.996835,0.719937,0.487421,0.996855,0.996855,0.481132,1,1,0.5,0.993711,1,0.996855,0.737342,0.455975,0.746835,0.996855,0.737342,0.746835,0.993671,0.996855,0.996855,0.474684,1,0.974684,1,0.998418,0.987421,0.481013,0.496855,0.493711,0.995253,0.996855,0.982595,0.734177,0.746835,0.996835,0.481132,0.484177,0.974684,0.993671,0.484277,0.742089,0.474843,0.981013,0.996835,0.477848,0.743671,1,0.471698,0.742089,0.990506,0.740506,0.996855,0.996855,1,0.992089,0.746835,0.996855,0.990506,0.996855,0.477987,0.727848,0.735759,0.993711,0.737342,0.737342,0.477987,0.734177]
area_under_roc_curve
0.8547106370909962 [0.738924,0.998418,1,0.996855,0.746835,0.487342,0.993711,0.977848,0.996835,0.743671,0.996835,0.993671,0.493711,0.985759,1,0.988924,0.746835,0.962025,0.462264,0.458861,0.965409,1,0.746835,1,0.490566,0.985759,0.738924,0.996855,0.992089,0.746835,0.993711,0.996835,0.742089,0.974684,0.996855,0.496855,0.996855,0.924528,0.996855,1,0.735759,0.977987,1,0.977987,1,0.996855,0.992089,1,0.955696,0.990506,0.993671,0.996855,0.996855,0.474684,0.996855,0.96519,1,0.998418,1,0.982595,0.990566,0.981132,0.96519,0.484277,0.745253,0.987342,0.735759,1,0.921384,0.734177,0.727848,0.737342,0.990566,0.969937,0.974843,0.746835,0.746835,0.481013,1,0.988924,0.487421,0.474684,1,0.702532,0.955975,0.5,0.996855,0.984177,0.455696,0.984277,1,0.996855,0.471698,0.477848,0.998418,0.496855,0.740506,0.973101,0.933962,0.710443]
area_under_roc_curve
0.7970923145052144 [0.740506,1,0.75,1,0.490566,0.743671,0.477987,0.738924,0.487421,0.75,0.981013,0.993671,0.996855,0.468553,0.993711,0.742089,0.75,0.993671,0.996855,0.993671,0.481013,0.716772,1,0.996835,0.996835,0.71519,0.731013,1,1,0.745253,0.996855,0.477987,0.487342,0.46519,0.984277,1,0.723101,0.484177,0.988924,0.984177,0.992089,0.745253,0.5,0.993711,0.996855,0.481132,0.998418,0.496835,0.746835,0.984177,0.981132,0.477987,0.998418,0.959119,0.740506,0.468354,0.996855,0.731013,0.988924,0.484277,0.746835,0.742089,0.996855,0.493711,0.745253,0.471519,0.471698,0.998418,0.477848,0.710443,0.735759,0.982595,0.996855,0.490566,0.969937,1,0.746835,0.699367,1,0.987421,0.465409,0.996835,0.477848,0.738924,0.993711,1,0.487421,1,1,0.735759,0.996855,0.987342,0.940252,0.993711,0.748418,0.996855,0.734177,0.993711,0.459119,0.734177]
area_under_roc_curve
0.8297464622641508 [0.740506,1,1,0.990506,0.993711,0.993671,0.996855,0.743671,0.993711,0.745253,0.990506,1,0.993711,0.471698,0.996855,0.740506,1,0.992089,0.977987,0.732595,1,0.740506,0.75,1,0.734177,0.731013,0.726266,1,1,1,1,1,0.738924,0.981013,0.490566,1,0.732595,0.734177,0.998418,0.996835,0.996835,0.735759,1,0.996855,0.996855,0.984277,1,0.746835,0.727848,0.745253,0.993711,0.968553,0.984177,0.974843,0.487342,0.474684,0.993711,0.996835,0.993671,0.996855,0.745253,0.727848,0.493711,0.95283,0.743671,0.743671,0.474843,1,0.727848,0.996835,0.474684,0.990506,0.949686,0.996855,0.462025,0.993711,0.745253,0.721519,0.993671,0.490566,0.996855,0.490506,0.735759,0.474684,0.487421,0.487421,0.971698,0.477987,0.484177,0.990506,1,0.990506,0.487421,0.981132,0.988924,0.996855,0.982595,0.484277,0.465409,0.742089]
area_under_roc_curve
0.8348687156675428 [0.987342,0.487421,1,0.982595,1,0.996835,0.984177,0.996835,0.993711,0.735759,1,0.996855,0.740506,0.965409,0.490566,0.724684,0.493711,0.477987,0.708861,0.97943,0.738924,0.996835,0.740506,1,0.735759,0.474843,0.981132,0.742089,0.481132,1,0.998418,0.5,0.996855,0.993711,1,0.75,0.990506,0.738924,0.998418,0.493711,0.740506,0.740506,0.740506,0.984177,0.996855,0.981013,0.746835,0.748418,0.971698,1,0.993711,0.738924,0.748418,0.959119,0.740506,0.721519,0.996835,0.740506,0.984177,0.933962,0.987342,0.712025,0.990566,0.481013,0.735759,0.990566,0.468553,0.998418,0.724684,0.458861,0.990566,1,0.732595,1,0.452532,0.990566,1,0.971698,1,0.996855,0.995253,0.990566,0.740506,0.987342,1,0.996835,0.72943,0.471698,1,0.5,0.996855,0.996835,0.738924,0.993711,0.490566,0.742089,0.987342,0.493711,0.71519,0.996835]
area_under_roc_curve
0.8209092130403632 [1,0.993711,1,0.477848,1,0.716772,0.993671,0.996835,0.493711,0.998418,0.993711,0.487421,0.742089,0.981132,0.487421,0.468354,1,0.465409,0.971519,0.490506,0.977848,0.738924,0.734177,1,0.990506,0.446541,0.462264,0.742089,1,0.742089,0.981013,0.996855,1,1,0.740506,1,0.740506,0.984177,0.993671,0.996855,0.996835,0.737342,0.993671,0.490506,1,0.740506,1,1,0.962264,0.496855,0.949686,0.719937,0.738924,1,0.743671,0.723101,1,0.993671,0.481013,0.996855,0.493671,0.734177,0.971698,0.738924,0.748418,0.984277,0.993711,0.746835,0.481013,0.737342,0.471698,1,0.996835,0.474843,0.452532,1,0.987421,0.930818,1,0.962264,0.968354,1,0.732595,0.990506,0.710443,0.738924,0.990506,0.484277,1,0.740506,1,0.996835,0.743671,0.971698,0.474843,0.987342,0.987342,0.996855,0.691456,0.726266]
area_under_roc_curve
0.8546712045219328 [0.471698,1,1,1,0.990506,0.990566,0.477848,0.477987,0.981013,1,0.493711,1,0.996835,0.746835,0.996835,0.962264,1,0.981132,1,1,0.998418,1,0.993711,1,0.748418,0.449686,0.465409,0.993671,0.731013,0.996855,0.996835,1,0.484277,0.977987,0.996835,1,0.738924,0.735759,0.996835,0.996855,0.487421,0.737342,0.746835,0.737342,0.490506,0.748418,1,0.727848,0.471698,0.996855,0.987342,0.97943,0.993671,0.705696,0.740506,0.471698,1,0.981132,0.735759,0.981013,0.746835,1,0.995253,0.740506,1,0.987421,0.985759,0.5,0.734177,0.987421,0.984277,0.496855,0.745253,1,0.471519,0.738924,0.996855,0.474843,1,0.996835,1,0.990566,0.465409,0.955975,0.995253,1,0.958861,0.713608,0.471698,0.996835,1,0.746835,0.996835,0.71519,0.993711,0.726266,0.955975,0.987342,0.737342,1]
area_under_roc_curve
0.8582455119019186 [0.962264,0.484177,0.496855,0.985759,1,0.987421,0.734177,0.484277,0.737342,0.996855,1,0.993711,0.993671,0.987342,0.742089,1,1,1,0.993671,1,1,0.996835,1,1,1,0.465409,1,0.743671,0.487342,0.993711,0.745253,0.75,1,0.946541,0.992089,0.734177,0.707278,0.990506,1,1,0.971698,0.981013,0.748418,0.743671,1,0.738924,1,0.72943,0.993711,0.493711,0.995253,0.732595,0.996835,0.955696,0.740506,0.468553,1,0.987421,0.477848,1,0.993671,0.721519,0.996835,0.734177,1,0.5,0.958861,1,0.987342,0.471698,0.477987,0.993711,0.740506,0.996835,0.734177,0.995253,1,0.474843,0.481132,0.743671,0.993671,0.487421,0.996855,0.490566,0.987342,1,0.734177,0.974684,0.45283,0.990506,1,0.996835,0.996835,0.737342,1,1,1,0.477848,0.995253,0.984277]
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
0.45054077524275676
kappa
0.4631932109729623
kappa
0.41273975846554584
kappa
0.4188709040663245
kappa
0.3810200536870362
kappa
0.41921483527322945
kappa
0.36234857751647404
kappa
0.3874329017998105
kappa
0.4885152735022496
kappa
0.4946703513620213
kb_relative_information_score
91.59672097718149
kb_relative_information_score
90.90545589253892
kb_relative_information_score
83.19553710387359
kb_relative_information_score
89.40375024172317
kb_relative_information_score
77.83506802820685
kb_relative_information_score
86.37149423714054
kb_relative_information_score
85.04823860235508
kb_relative_information_score
82.24399276536087
kb_relative_information_score
93.41155547373995
kb_relative_information_score
95.0891912085144
mean_absolute_error
0.012511882054456556
mean_absolute_error
0.012513646645859309
mean_absolute_error
0.012988840676437432
mean_absolute_error
0.013197422320434127
mean_absolute_error
0.013749039643384652
mean_absolute_error
0.013130426488393788
mean_absolute_error
0.013636540336598394
mean_absolute_error
0.013498344359136738
mean_absolute_error
0.01236395074599893
mean_absolute_error
0.012275950967354798
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
0.45625
predictive_accuracy
0.46875
predictive_accuracy
0.41875
predictive_accuracy
0.425
predictive_accuracy
0.3875
predictive_accuracy
0.425
predictive_accuracy
0.36875
predictive_accuracy
0.39375
predictive_accuracy
0.49375
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
0.45625 [0,0.5,1,0,0.5,1,0.5,1,0,0,1,0.5,0,0,1,1,1,0,0,0,1,0,1,1,0,0.5,0.5,0.5,0.5,0,1,1,0.5,0,1,0,0,0,1,1,1,0,1,1,1,0.5,1,1,0,0.5,0.5,0.5,1,0,1,0,1,1,0,0,0,1,0,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,0.5,0.5,0,0.5,0.5,1,0,0,1,0.5,0.5,0,0,0,0,0.5,1,1,0,0.5,0.5,0,0,0,1]
recall
0.46875 [1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,0,1,0,0.5,1,1,0,0,1,1,0,0,0,0.5,1,1,1,0.5,0.5,1,1,0,0,0,1,0,1,1,0.5,0.5,0,0,0,0,1,0,0.5,1,0.5,1,0,1,1,0,0,1,1,1,0,1,1,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,0,0,0,0.5,0,0,0,0,0,0,1,0,0,0,0.5,1,0,0,0]
recall
0.41875 [0,0.5,0,1,0.5,0,0,1,0.5,0,0.5,0.5,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,1,1,0,1,1,0,0,1,1,0,1,1,0,1,1,1,0,0,0.5,1,0.5,0.5,1,1,0,0,1,0,1,0.5,0,0,0,0,0.5,1,0,0.5,0,1,0,0,0,1,0,0.5,0,0,1,0,0.5,1,0,0,1,0.5,1,0,1,0,0.5,1,0.5,1,0,0,0,1,0,0.5,0,0.5]
recall
0.425 [0.5,1,1,0,0.5,0,1,0.5,0,0,1,0.5,0,0,1,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0,0.5,0.5,1,1,0.5,0.5,1,0,1,0,1,1,0,0,1,0,1,1,0.5,1,0,1,1,1,0,0,1,0,1,1,0,0,1,0,1,0,0.5,1,0,1,0,0.5,0,0,1,0.5,0,0.5,0,0,1,0.5,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0.5,0,0.5,0,0,0]
recall
0.3875 [0.5,1,0.5,1,0,0.5,0,0,0,0.5,0,0.5,0,0,1,0,0.5,0.5,1,1,0,0,1,1,1,0,0,1,1,0.5,1,0,0,0,0,1,0.5,0,0.5,0.5,1,0.5,0,1,1,0,0.5,0,0.5,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0.5,0.5,1,0,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0.5,0,0.5,0,0,1,0,0.5,0,0,0,1,1,0,0,1,0,1,0.5,0,0.5,1,0,0]
recall
0.425 [0,1,1,0,1,0,0,0.5,1,0.5,0.5,1,1,0,0,0,1,1,0,0.5,1,0.5,0,1,0.5,0,0,1,1,1,1,1,0.5,0,0,1,0,0.5,1,0,0.5,0.5,1,1,1,0,1,0.5,0,0.5,1,0,0,0,0,0,1,1,1,1,0.5,0,0,0,0.5,0.5,0,1,0.5,0.5,0,1,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0.5,0,0,0.5]
recall
0.36875 [0.5,0,1,0,1,0,0,1,1,0,1,1,0.5,0,0,0,0,0,0,0.5,0,1,0.5,1,0,0,0,0.5,0,1,0.5,0,1,1,1,0.5,0,0.5,1,0,0,0.5,0.5,0,1,0,0,0.5,0,1,1,0,0.5,0,0,0,1,0.5,0,0,0,0,1,0,0,1,0,0.5,0.5,0,0,1,0,0,0,1,1,0,1,1,0.5,0,0.5,0,1,0.5,0,0,0,0,1,1,0.5,1,0,0.5,0.5,0,0,0]
recall
0.39375 [0.5,1,1,0,1,0,0.5,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0.5,0,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0.5,0,0.5,1,1,0.5,1,0,1,0.5,1,1,0,0,0,0,0.5,1,0,0,1,0.5,0,1,0,0.5,0,0.5,0.5,0,0,0.5,0,0.5,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0.5,0,1,0,0,0,1,0.5,0,0,0]
recall
0.49375 [0,1,1,0,0,1,0,0,0,1,0,1,1,0,1,0,1,0,1,1,0.5,1,1,1,0.5,0,0,1,0,1,1,1,0,0,1,1,0.5,0,1,0,0,0.5,0,0.5,0,0.5,1,0.5,0,1,1,0.5,1,0,0,0,1,0,0,0,0,1,1,0.5,1,1,0.5,0,0.5,0,1,0,0.5,1,0,0.5,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,1,1,0,0,0,1,0.5,0,0.5,0,1]
recall
0.5 [0,0,0,0,1,0,0,0,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,0.5,0,1,0,0.5,1,0,0.5,0.5,0,1,1,1,0,0.5,0.5,0.5,1,0,1,0.5,1,0,1,0,1,0,0,0,1,1,0,1,1,0,0,0.5,1,0,0,1,0.5,0,0,0,0,1,0.5,0.5,1,0,0,0.5,0.5,0,1,0,1,1,0,0,0,0.5,0.5,1,1,0.5,0,1,1,0,0,0]
relative_absolute_error
0.6319132350735623
relative_absolute_error
0.6320023558514792
relative_absolute_error
0.6560020543655258
relative_absolute_error
0.6665364808300054
relative_absolute_error
0.6943959415850823
relative_absolute_error
0.6631528529491801
relative_absolute_error
0.6887141584140593
relative_absolute_error
0.6817345635927634
relative_absolute_error
0.6244419568686318
relative_absolute_error
0.6199975236037767
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
0.0868560085014353
root_mean_squared_error
0.08611485713271444
root_mean_squared_error
0.09133408677793396
root_mean_squared_error
0.08808813749129232
root_mean_squared_error
0.09317843932904535
root_mean_squared_error
0.08984988527116687
root_mean_squared_error
0.09197413995648933
root_mean_squared_error
0.09060190878340893
root_mean_squared_error
0.08517711632861692
root_mean_squared_error
0.08496296398968788
root_relative_squared_error
0.8729357302641803
root_relative_squared_error
0.8654868787402243
root_relative_squared_error
0.9179421103398998
root_relative_squared_error
0.8853190925450147
root_relative_squared_error
0.9364785509252671
root_relative_squared_error
0.9030253239422436
root_relative_squared_error
0.9243748868221497
root_relative_squared_error
0.9105834446199168
root_relative_squared_error
0.8560622290499286
root_relative_squared_error
0.8539099170614296
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
224.52256999895326
usercpu_time_millis
215.0591669997084
usercpu_time_millis
164.20795700105373
usercpu_time_millis
154.9991140018392
usercpu_time_millis
155.1703059958527
usercpu_time_millis
154.606321000756
usercpu_time_millis
154.6444590021565
usercpu_time_millis
155.13975599969854
usercpu_time_millis
154.73020300123608
usercpu_time_millis
154.08571399893845
usercpu_time_millis_testing
1.6978540006675757
usercpu_time_millis_testing
1.7321800005447585
usercpu_time_millis_testing
1.2945179987582378
usercpu_time_millis_testing
1.2513729998318013
usercpu_time_millis_testing
1.2529409978014883
usercpu_time_millis_testing
1.2593760002346244
usercpu_time_millis_testing
1.2514689988165628
usercpu_time_millis_testing
1.276208000490442
usercpu_time_millis_testing
1.2684410030487925
usercpu_time_millis_testing
1.2497940006142017
usercpu_time_millis_training
222.82471599828568
usercpu_time_millis_training
213.32698699916364
usercpu_time_millis_training
162.9134390022955
usercpu_time_millis_training
153.7477410020074
usercpu_time_millis_training
153.9173649980512
usercpu_time_millis_training
153.34694500052137
usercpu_time_millis_training
153.39299000333995
usercpu_time_millis_training
153.8635479992081
usercpu_time_millis_training
153.46176199818728
usercpu_time_millis_training
152.83591999832424