10095193
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)
8020321
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
7
8783
min_samples_split
10
8783
min_weight_fraction_leaf
0.0
8783
presort
false
8783
random_state
43500
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
21110923
description
https://api.openml.org/data/download/21110923/description.xml
-1
21110924
predictions
https://api.openml.org/data/download/21110924/predictions.arff
area_under_roc_curve
0.808621764520202 [0.740057,0.870522,0.843355,0.903192,0.903942,0.802083,0.774167,0.870443,0.774858,0.841521,0.869003,0.869456,0.838088,0.738518,0.871863,0.737216,0.905106,0.735697,0.761857,0.703224,0.807252,0.869673,0.804885,0.999605,0.806226,0.697029,0.77109,0.806246,0.838798,0.873047,0.871626,0.935882,0.807765,0.671954,0.837693,0.871942,0.768288,0.738419,0.997021,0.934245,0.772688,0.835819,0.841343,0.835957,0.871725,0.771721,0.93458,0.776515,0.705966,0.80883,0.896918,0.704111,0.836608,0.694129,0.806029,0.6337,0.999369,0.900765,0.799321,0.798769,0.714765,0.770103,0.839094,0.647885,0.838522,0.773497,0.669863,0.935172,0.573587,0.671638,0.73329,0.87137,0.773714,0.868608,0.56686,0.933554,0.840475,0.665384,0.904179,0.870837,0.804569,0.677991,0.771563,0.771307,0.829447,0.840791,0.831459,0.765625,0.739721,0.774996,0.966422,0.965278,0.829052,0.795553,0.839291,0.837674,0.834714,0.739583,0.694149,0.802872]
average_cost
0
f_measure
0.44636935357678453 [0.363636,0.628571,0.785714,0.692308,0.684211,0.272727,0.457143,0.666667,0.421053,0.666667,0.545455,0.62069,0.5,0.222222,0.705882,0.133333,0.764706,0.137931,0.25641,0.387097,0.457143,0.606061,0.378378,0.914286,0.428571,0.210526,0.230769,0.551724,0.642857,0.666667,0.545455,0.742857,0.6,0.210526,0.47619,0.666667,0.384615,0.25,0.8,0.727273,0.4375,0.434783,0.666667,0.424242,0.6875,0.375,0.642857,0.5,0.181818,0.645161,0.512821,0.25,0.37037,0.129032,0.466667,0.052632,0.882353,0.514286,0.117647,0.285714,0.434783,0.387097,0.571429,0.32,0.5625,0.363636,0.125,0.666667,0.142857,0.230769,0.114286,0.625,0.451613,0.6,0.086957,0.647059,0.516129,0.129032,0.72,0.529412,0.484848,0.148148,0.411765,0.277778,0.304348,0.571429,0.24,0.181818,0.230769,0.45,0.705882,0.588235,0.4,0.1875,0.551724,0.484848,0.424242,0.285714,0.086957,0.416667]
kappa
0.4438131313131313
kb_relative_information_score
853.7929969970668
mean_absolute_error
0.012295829136141518
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
0.4607360140196323 [0.352941,0.578947,0.916667,0.9,0.590909,0.5,0.421053,0.647059,0.363636,0.647059,0.529412,0.692308,0.5,0.2,0.666667,0.142857,0.722222,0.153846,0.217391,0.4,0.421053,0.588235,0.333333,0.842105,0.5,0.181818,0.3,0.615385,0.75,0.6,0.529412,0.684211,0.642857,0.181818,0.384615,0.818182,0.5,0.25,0.736842,0.705882,0.4375,0.333333,0.714286,0.411765,0.6875,0.375,0.75,0.5,0.176471,0.666667,0.434783,0.25,0.454545,0.133333,0.5,0.045455,0.833333,0.473684,0.111111,0.263158,0.714286,0.4,0.526316,0.444444,0.5625,0.352941,0.125,0.647059,0.166667,0.3,0.105263,0.625,0.466667,0.642857,0.142857,0.611111,0.533333,0.133333,1,0.5,0.470588,0.181818,0.388889,0.25,0.233333,0.666667,0.333333,0.176471,0.3,0.375,0.666667,0.555556,0.428571,0.1875,0.615385,0.470588,0.411765,0.333333,0.142857,0.625]
predictive_accuracy
0.449375
prior_entropy
6.6438561897747395
recall
0.449375 [0.375,0.6875,0.6875,0.5625,0.8125,0.1875,0.5,0.6875,0.5,0.6875,0.5625,0.5625,0.5,0.25,0.75,0.125,0.8125,0.125,0.3125,0.375,0.5,0.625,0.4375,1,0.375,0.25,0.1875,0.5,0.5625,0.75,0.5625,0.8125,0.5625,0.25,0.625,0.5625,0.3125,0.25,0.875,0.75,0.4375,0.625,0.625,0.4375,0.6875,0.375,0.5625,0.5,0.1875,0.625,0.625,0.25,0.3125,0.125,0.4375,0.0625,0.9375,0.5625,0.125,0.3125,0.3125,0.375,0.625,0.25,0.5625,0.375,0.125,0.6875,0.125,0.1875,0.125,0.625,0.4375,0.5625,0.0625,0.6875,0.5,0.125,0.5625,0.5625,0.5,0.125,0.4375,0.3125,0.4375,0.5,0.1875,0.1875,0.1875,0.5625,0.75,0.625,0.375,0.1875,0.5,0.5,0.4375,0.25,0.0625,0.3125]
relative_absolute_error
0.6210014715222978
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.09029211811310586
root_relative_squared_error
0.9074699312352257
total_cost
0
area_under_roc_curve
0.8138767813072206 [1,0.988924,1,0.493711,0.996835,0.996855,0.745253,1,0.75,1,0.996835,0.746835,0.738924,0.745253,1,0.955975,0.998418,0.487342,0.487342,0.481132,0.996855,0.484277,0.484277,1,0.996855,0.737342,0.743671,0.990506,0.748418,0.5,0.990506,1,0.746835,0.484177,1,0.487421,0.490566,0.493711,1,1,1,0.496855,0.75,0.993671,0.993671,0.742089,1,1,0.481013,0.743671,0.748418,0.740506,0.990566,0.474684,0.996855,0.981132,0.996835,0.993711,0.974843,0.738924,0.496855,1,0.75,0.490506,1,0.746835,0.982595,1,0.471698,0.984277,0.723101,1,0.743671,0.742089,0.468553,0.996835,0.998418,0.982595,0.746835,0.996835,1,0.496835,0.993711,0.996855,0.735759,0.746835,0.958861,0.726266,1,0.490566,0.988924,1,0.996835,0.987342,0.992089,0.490506,0.996855,0.493671,0.471519,1]
area_under_roc_curve
0.8039290761085901 [1,1,0.75,1,0.996835,1,0.493671,1,0.746835,1,0.75,0.742089,0.746835,0.743671,1,1,1,0.738924,0.737342,0.993711,1,0.981132,0.987421,1,1,0.72943,0.740506,0.493671,0.996835,1,0.75,1,0.998418,0.737342,0.732595,1,0.484277,0.993711,0.993711,1,0.493711,0.996855,1,0.743671,0.746835,0.735759,0.993711,0.981132,0.477848,1,0.71519,0.462025,0.996855,0.987342,1,0.484277,1,0.487421,0.990566,0.735759,1,0.987421,0.998418,0.496835,1,0.977848,0.743671,0.987421,0.474843,0.490566,0.727848,1,0.743671,0.998418,0.455975,0.998418,0.742089,0.72943,0.998418,0.75,0.742089,0.496835,0.468553,0.471698,0.737342,0.742089,0.738924,0.474684,0.746835,0.490566,0.745253,0.996855,0.97943,0.732595,0.745253,1,1,0.490506,0.721519,0.490566]
area_under_roc_curve
0.8013358560226095 [0.487342,0.746835,0.490506,1,0.746835,0.72943,0.493711,1,0.990506,0.746835,0.735759,0.748418,1,0.746835,0.481013,0.738924,0.998418,0.710443,0.481132,0.481013,1,0.996855,0.723101,0.996855,0.493711,0.996835,1,0.996855,1,0.996835,0.993711,0.996835,1,0.738924,0.487421,0.996855,0.996855,0.484277,1,1,0.493671,0.993711,1,1,0.738924,0.484277,0.748418,0.996855,0.743671,0.748418,0.987342,0.996855,0.990566,0.484177,1,0.740506,1,0.992089,0.987421,0.484177,0.496855,0.493711,1,1,0.992089,0.490506,0.748418,0.996835,0.490566,0.484177,0.993671,0.996835,0.484277,0.738924,0.481132,1,0.996835,0.481013,0.745253,1,0.477987,0.742089,0.995253,0.743671,0.477987,1,1,0.981013,0.746835,0.996855,0.990506,1,0.490566,0.731013,0.735759,0.987421,0.737342,0.988924,0.484277,0.742089]
area_under_roc_curve
0.798792766300454 [0.742089,0.998418,1,1,0.748418,0.490506,0.993711,0.990506,0.745253,0.746835,0.71519,0.992089,0.493711,0.740506,0.996835,0.995253,0.745253,0.72943,0.477987,0.474684,0.481132,1,0.746835,1,0.496855,0.971519,0.743671,0.996855,0.988924,0.748418,1,0.998418,0.745253,0.471519,0.996855,0.496855,0.996855,0.977987,0.996855,1,0.745253,0.990566,1,0.987421,1,0.996855,0.992089,1,0.987342,1,0.996835,0.949686,0.477987,0.471519,0.996855,0.474684,1,0.998418,0.968553,0.97943,0.484277,0.487421,0.72943,0.496855,0.748418,0.987342,0.484177,1,0.965409,0.742089,0.738924,0.487342,0.996855,0.990506,0.462264,0.748418,0.496835,0.471519,1,0.745253,0.490566,0.493671,1,0.732595,0.965409,0.5,0.993711,0.987342,0.471519,0.984277,1,0.996855,0.477987,0.481013,1,0.5,0.490506,0.990506,0.943396,0.71519]
area_under_roc_curve
0.7758258448770001 [0.746835,1,0.75,1,0.493711,0.5,0.487421,0.738924,0.490566,0.746835,0.987342,0.987342,1,0.474843,0.993711,0.493671,0.75,0.743671,0.996855,0.993671,0.490506,0.738924,1,1,0.743671,0.471519,0.737342,1,1,0.745253,0.493711,1,0.496835,0.490506,0.990566,1,0.723101,0.490506,0.995253,0.990506,1,0.746835,0.5,0.984277,0.996855,0.490566,0.998418,0.5,0.746835,0.740506,0.984277,0.481132,0.748418,0.484277,0.738924,0.710443,1,0.734177,0.996835,0.490566,0.746835,0.745253,1,0.496855,0.746835,0.487342,0.477987,1,0.487342,0.708861,0.740506,0.993671,1,0.490566,0.969937,1,0.746835,0.477848,1,0.987421,0.471698,0.998418,0.487342,0.742089,1,0.996855,0.493711,1,1,0.746835,1,0.987342,0.921384,0.996855,0.748418,1,0.742089,0.974843,0.481132,0.737342]
area_under_roc_curve
0.8129077601305628 [0.742089,1,1,1,1,0.993671,1,0.746835,0.993711,0.745253,0.987342,1,0.993711,0.984277,1,0.745253,1,0.992089,0.477987,0.484177,0.75,0.742089,0.75,1,0.738924,0.731013,0.72943,1,1,1,1,1,0.735759,0.487342,0.493711,0.996835,0.737342,0.734177,1,0.996835,0.735759,0.743671,1,1,0.996855,0.984277,1,0.746835,0.474684,0.745253,0.987421,0.981132,0.97943,0.974843,0.738924,0.474684,1,0.996835,0.993671,0.996855,0.735759,0.737342,0.496855,0.990566,0.743671,0.743671,0.477987,0.995253,0.726266,0.748418,0.484177,0.987342,0.481132,1,0.484177,0.990566,0.746835,0.718354,0.993671,0.496855,0.996855,0.490506,0.742089,0.481013,0.490566,1,0.987421,0.490566,0.484177,0.990506,1,0.987342,0.493711,0.993711,0.995253,0.990566,0.97943,0.484277,0.465409,0.745253]
area_under_roc_curve
0.8285163402595334 [0.993671,0.493711,1,0.987342,1,0.993671,1,1,0.990566,0.745253,1,0.996855,0.743671,0.477987,0.493711,0.735759,0.493711,0.487421,0.71519,0.996835,0.742089,0.996835,0.992089,1,0.740506,0.481132,0.981132,0.743671,0.490566,1,1,0.5,1,0.993711,1,0.75,0.993671,0.743671,0.995253,0.493711,0.735759,0.987342,0.743671,0.987342,0.996855,0.982595,0.746835,0.745253,0.977987,1,0.987421,0.742089,0.996835,0.933962,0.742089,0.732595,0.998418,0.742089,0.737342,0.955975,0.75,0.713608,0.993711,0.496835,0.735759,0.987421,0.474843,1,0.477848,0.474684,1,1,0.742089,1,0.462025,0.996855,1,0.996855,1,0.996855,0.995253,0.993711,0.738924,0.993671,1,0.743671,0.727848,0.484277,0.993711,0.5,1,0.996835,0.734177,0.471698,0.493711,0.743671,0.998418,0.496855,0.724684,0.998418]
area_under_roc_curve
0.7991676866889579 [0.746835,1,1,0.490506,0.996855,0.727848,0.995253,0.993671,0.493711,0.998418,1,0.493711,0.745253,0.987421,0.493711,0.481013,1,0.481132,0.952532,0.487342,0.737342,0.742089,0.740506,1,0.987342,0.474843,0.477987,0.745253,1,0.746835,0.993671,0.996855,1,1,0.740506,1,0.742089,1,0.995253,1,0.996835,0.738924,0.990506,0.496835,1,0.75,1,1,0.965409,0.496855,0.955975,0.490506,0.490506,1,0.737342,0.731013,1,0.998418,0.734177,0.990566,0.496835,0.740506,0.993711,0.737342,0.746835,0.996855,0.471698,0.75,0.487342,0.731013,0.474843,0.748418,0.996835,0.481132,0.474684,1,0.993711,0.937107,1,0.481132,0.719937,0.987421,0.732595,0.993671,0.708861,0.743671,0.746835,0.487421,0.987421,0.740506,1,1,0.746835,0.971698,0.481132,1,0.974684,0.990566,0.721519,0.72943]
area_under_roc_curve
0.823194187365656 [0.481132,0.995253,1,1,1,0.996855,0.740506,0.487421,0.735759,1,0.496855,0.996855,0.996835,0.746835,0.996835,0.487421,0.993711,0.981132,1,1,0.996835,1,0.993711,1,0.75,0.45283,0.471698,0.743671,0.742089,1,0.996835,1,0.481132,0.987421,0.988924,1,0.740506,0.735759,1,0.5,0.490566,0.743671,0.75,0.727848,0.493671,0.748418,0.996855,0.738924,0.490566,1,0.743671,0.740506,0.745253,0.723101,0.742089,0.484277,1,0.990566,0.487342,0.734177,0.746835,0.996835,0.995253,0.742089,1,1,0.97943,0.5,0.732595,0.993711,0.984277,0.5,0.745253,1,0.474684,0.740506,0.996855,0.484277,1,1,1,0.987421,0.487421,0.993711,0.987342,1,0.952532,0.732595,0.484277,0.745253,1,0.746835,1,0.72943,1,0.734177,0.477987,0.740506,0.732595,1]
area_under_roc_curve
0.8270109366292487 [0.474843,0.487342,0.5,0.996835,1,0.987421,0.740506,0.487421,0.738924,1,0.996855,1,0.996835,0.738924,0.996835,1,1,0.993711,0.984177,1,1,0.995253,0.496855,1,1,0.468553,1,0.746835,0.493671,0.987421,0.496835,0.75,1,0.977987,0.745253,0.742089,0.726266,0.740506,0.996835,1,0.971698,0.988924,0.748418,0.743671,1,0.740506,0.993711,0.490506,0.996855,0.493711,0.995253,0.743671,0.996835,0.708861,0.745253,0.468553,1,0.993711,0.493671,1,1,0.72943,0.487342,0.745253,0.993711,0.5,0.481013,0.996855,0.471519,0.481132,0.477987,0.987421,0.742089,0.996835,0.742089,0.993671,1,0.487421,0.496855,0.996835,0.746835,0.490566,1,0.493711,0.988924,1,0.742089,0.985759,0.471698,0.998418,1,0.996835,0.996835,0.990506,1,1,1,0.742089,0.995253,0.996855]
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
0.4503889130177281
kappa
0.44451019844557543
kappa
0.425232906995105
kappa
0.41923774954627946
kappa
0.41893972289109066
kappa
0.41273975846554584
kappa
0.4318854302284294
kappa
0.4000631512472371
kappa
0.5009475679090335
kappa
0.5325885278907267
kb_relative_information_score
87.56954001097156
kb_relative_information_score
85.69149844067975
kb_relative_information_score
83.98401454437571
kb_relative_information_score
81.74281190053672
kb_relative_information_score
78.41749602351905
kb_relative_information_score
84.17641760422903
kb_relative_information_score
88.3723912059569
kb_relative_information_score
80.86522973372652
kb_relative_information_score
89.6293159027603
kb_relative_information_score
93.34428163032057
mean_absolute_error
0.011926018426018427
mean_absolute_error
0.01197917776667777
mean_absolute_error
0.012310061813186815
mean_absolute_error
0.012814200729825725
mean_absolute_error
0.01261101884226885
mean_absolute_error
0.012830724136974139
mean_absolute_error
0.01251280212842713
mean_absolute_error
0.01295080266955267
mean_absolute_error
0.011896791749916752
mean_absolute_error
0.011126693098568102
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
0.45
predictive_accuracy
0.43125
predictive_accuracy
0.425
predictive_accuracy
0.425
predictive_accuracy
0.41875
predictive_accuracy
0.4375
predictive_accuracy
0.40625
predictive_accuracy
0.50625
predictive_accuracy
0.5375
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 [1,0.5,1,0,1,1,0.5,1,0.5,1,1,0.5,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0.5,0,0.5,0,1,1,0.5,0,1,0,0,0,1,1,1,0,0.5,1,1,0.5,1,1,0,0.5,0.5,0.5,0,0,1,0,1,1,0,0.5,0,1,0.5,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,1,0.5,0.5,0.5,0.5,1,0,1,1,0.5,0.5,0,0,0,0,0.5,0,1,0,0,0,0,0,0,1]
recall
0.45 [1,1,0.5,1,1,1,0,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,0,0.5,1,1,0,0,1,1,0,0,0,0.5,1,0.5,1,0.5,0.5,0.5,1,0,1,1,1,0,1,1,0.5,0.5,0,0,0,0,1,0,0,1,0.5,1,0,1,0,0,0,1,1,0.5,0,1,0,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,0,0,0,0.5,0,0.5,0,0,0,0.5,1,0.5,0,0.5,0.5,1,0,0,0]
recall
0.43125 [0,0.5,0,1,0.5,0,0,1,1,0.5,0.5,0.5,1,0.5,0,0,1,0,0,0,1,0,0,1,0,1,0,1,1,1,1,1,1,0.5,0,0,1,0,1,1,0,1,1,1,0,0,0.5,1,0.5,0.5,1,1,0,0,1,0.5,1,0.5,0,0,0,0,1,1,0.5,0,0,1,0,0,0,1,0,0.5,0,0,1,0,0,1,0,0,0.5,0.5,0,0,1,0.5,0.5,1,0.5,1,0,0,0,0,0,0,0,0.5]
recall
0.425 [0.5,1,1,1,0.5,0,1,0.5,0,0.5,0,0.5,0,0,1,0,0.5,0,0,0,0,1,0.5,1,0,0.5,0.5,1,0.5,0.5,1,0.5,0.5,0,1,0,0,0,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,0,0,0,1,0,1,1,0,0,0,0,0.5,0,0.5,1,0,1,0,0.5,0,0,1,0.5,0,0.5,0,0,1,0.5,0,0,1,0,1,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0]
recall
0.425 [0.5,1,0.5,1,0,0,0,0.5,0,0.5,1,0.5,1,0,1,0,0.5,0,1,1,0,0.5,1,1,0.5,0,0,1,1,0.5,0,1,0,0,1,1,0.5,0,0.5,1,1,0.5,0,1,1,0,0.5,0,0.5,0.5,0,0,0,0,0,0,1,0,1,0,0.5,0.5,1,0,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0.5,0,0.5,0,0,0.5,0,0.5,0,1,0,1,1,0.5,1,1,0,1,0.5,1,0.5,0,0,0]
recall
0.41875 [0,1,1,0,1,0,1,0.5,1,0.5,0.5,1,0,1,1,0,1,0.5,0,0,0.5,0.5,0.5,1,0,0.5,0,1,1,1,1,1,0.5,0,0,0,0,0.5,1,0,0.5,0.5,1,1,1,0,1,0.5,0,0.5,1,0,0,0,0,0,1,1,0,1,0.5,0,0,0,0.5,0.5,0,0.5,0.5,0.5,0,1,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,0.5,0,0,0.5]
recall
0.4375 [0.5,0,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0,0,0,0,0,0,0.5,0.5,1,0.5,1,0,0,0,0.5,0,1,0.5,0,1,1,1,0.5,1,0.5,1,0,0,1,0.5,0,1,0.5,0,0.5,0,1,1,0,0.5,0,0.5,0,0.5,0.5,0,0,0,0,1,0,0,1,0,1,0,0,1,1,0,0,0,1,1,0,1,1,0.5,0,0.5,0,0.5,0.5,0,0,0,0,1,1,0.5,0,0,0.5,1,0,0.5,0.5]
recall
0.40625 [0.5,1,1,0,1,0,1,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0.5,0.5,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0,0,0.5,1,1,0.5,1,0,1,0.5,1,1,0,0,0,0,0,1,0.5,0,1,0.5,0,1,0,0.5,1,0.5,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0,0,1,0,0,0,0.5,1,0,0.5,0.5,0,0,0.5,0,1,0,0,0,1,0.5,0,0,0]
recall
0.50625 [0,1,1,1,1,1,0.5,0,0.5,1,0,0,1,0.5,1,0,1,0,1,1,0.5,1,1,1,0.5,0,0,0.5,0.5,1,1,1,0,0,0.5,1,0.5,0,1,0,0,0.5,0,0,0,0.5,1,0.5,0,1,0.5,0.5,0.5,0,0,0,1,0,0,0,0,1,1,0.5,1,1,0.5,0,0.5,1,1,0,0.5,1,0,0.5,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,0.5,1,0,0,0,1,0.5,0,0.5,0,1]
recall
0.5375 [0,0,0,0.5,1,0,0,0,0.5,1,1,1,1,0,1,1,1,1,0.5,1,1,1,0,1,1,0,1,0.5,0,1,0,0.5,1,0,0.5,0.5,0,0.5,1,1,0,0.5,0.5,0.5,1,0.5,0,0,1,0,1,0.5,1,0,0.5,0,1,1,0,1,1,0,0,0.5,1,0,0,1,0,0,0,0,0,1,0.5,1,1,0,0,1,0.5,0,1,0,1,0.5,0,0.5,0,1,1,1,1,0.5,1,1,1,0.5,0,0]
relative_absolute_error
0.6023241629302226
relative_absolute_error
0.605008978115038
relative_absolute_error
0.6217202935952926
relative_absolute_error
0.6471818550417022
relative_absolute_error
0.6369201435489308
relative_absolute_error
0.6480163705542483
relative_absolute_error
0.6319597034559146
relative_absolute_error
0.6540809429066994
relative_absolute_error
0.6008480681776127
relative_absolute_error
0.5619541968973779
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
0.0897678274993853
root_mean_squared_error
0.0889440612573376
root_mean_squared_error
0.09232434575200137
root_mean_squared_error
0.09094116956478102
root_mean_squared_error
0.09260394593438483
root_mean_squared_error
0.09190075425017251
root_mean_squared_error
0.09032188182963481
root_mean_squared_error
0.0925583437749283
root_mean_squared_error
0.08764211295384126
root_mean_squared_error
0.08564932672981034
root_relative_squared_error
0.9022006123054797
root_relative_squared_error
0.8939214500635605
root_relative_squared_error
0.927894587498275
root_relative_squared_error
0.91399314376505
root_relative_squared_error
0.9307046750627626
root_relative_squared_error
0.9236373327226706
root_relative_squared_error
0.9077690678415684
root_relative_squared_error
0.9302463561156498
root_relative_squared_error
0.8808363772782966
root_relative_squared_error
0.8608081221495095
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
234.02769799577072
usercpu_time_millis
230.09217299841112
usercpu_time_millis
183.09209500148427
usercpu_time_millis
186.21933500253363
usercpu_time_millis
187.90746000377112
usercpu_time_millis
185.99043400172377
usercpu_time_millis
190.25465600498137
usercpu_time_millis
185.94350599960308
usercpu_time_millis
188.9881949973642
usercpu_time_millis
185.4880469982163
usercpu_time_millis_testing
1.7363119986839592
usercpu_time_millis_testing
1.7134949994215276
usercpu_time_millis_testing
1.3744159987254534
usercpu_time_millis_testing
1.3843930028087925
usercpu_time_millis_testing
1.5252400007739197
usercpu_time_millis_testing
1.4967780007282272
usercpu_time_millis_testing
1.4234020018193405
usercpu_time_millis_testing
1.4106740018178243
usercpu_time_millis_testing
1.4324309995572548
usercpu_time_millis_testing
1.3787269999738783
usercpu_time_millis_training
232.29138599708676
usercpu_time_millis_training
228.3786779989896
usercpu_time_millis_training
181.7176790027588
usercpu_time_millis_training
184.83494199972483
usercpu_time_millis_training
186.3822200029972
usercpu_time_millis_training
184.49365600099554
usercpu_time_millis_training
188.83125400316203
usercpu_time_millis_training
184.53283199778525
usercpu_time_millis_training
187.55576399780693
usercpu_time_millis_training
184.10931999824243