10111042
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)
8036313
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
1
8783
min_samples_split
17
8783
min_weight_fraction_leaf
0.0
8783
presort
false
8783
random_state
1166
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
21142621
description
https://api.openml.org/data/download/21142621/description.xml
-1
21142623
predictions
https://api.openml.org/data/download/21142623/predictions.arff
area_under_roc_curve
0.8171277225378791 [0.831992,0.870837,0.874092,0.90404,0.903685,0.866339,0.805102,0.838226,0.806088,0.840692,0.869811,0.837397,0.805556,0.766967,0.870462,0.735519,0.905382,0.765467,0.764579,0.702494,0.869535,0.867069,0.835267,1,0.774582,0.632221,0.735598,0.805516,0.838246,0.81035,0.870068,0.841304,0.774562,0.792771,0.837003,0.870324,0.797329,0.702573,0.996903,0.93529,0.80236,0.835286,0.872909,0.834103,0.86989,0.769926,0.934422,0.681621,0.701566,0.838088,0.897175,0.705374,0.930417,0.754222,0.806029,0.665503,0.999684,0.899779,0.832899,0.796875,0.74781,0.801847,0.804688,0.615294,0.839291,0.836687,0.763198,0.936435,0.633799,0.731297,0.665128,0.934343,0.772372,0.869358,0.594875,0.900982,0.871015,0.69332,0.902146,0.931818,0.867523,0.676926,0.737571,0.800821,0.8314,0.839923,0.894176,0.767736,0.735381,0.806463,0.934975,0.967152,0.829526,0.760969,0.839489,0.868924,0.86336,0.77253,0.72384,0.769018]
average_cost
0
f_measure
0.4525672845477093 [0.375,0.628571,0.785714,0.769231,0.702703,0.32,0.451613,0.666667,0.432432,0.588235,0.588235,0.6,0.424242,0.205128,0.666667,0.137931,0.8125,0.210526,0.333333,0.285714,0.545455,0.606061,0.358974,1,0.4375,0.129032,0.142857,0.5,0.642857,0.625,0.5625,0.647059,0.484848,0.216216,0.47619,0.645161,0.357143,0.1875,0.777778,0.75,0.424242,0.454545,0.705882,0.484848,0.6875,0.342857,0.592593,0.357143,0.171429,0.606061,0.487805,0.266667,0.387097,0.142857,0.516129,0.060606,0.969697,0.5,0.30303,0.258065,0.583333,0.375,0.45,0.26087,0.645161,0.411765,0.125,0.774194,0.222222,0.171429,0.102564,0.625,0.411765,0.580645,0.083333,0.588235,0.6,0.074074,0.692308,0.580645,0.62069,0.222222,0.4,0.193548,0.390244,0.62069,0.384615,0.148148,0.25,0.473684,0.685714,0.733333,0.482759,0.1875,0.5,0.555556,0.375,0.4,0.095238,0.384615]
kappa
0.4494949494949495
kb_relative_information_score
866.9951592785404
mean_absolute_error
0.012258030684593073
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
0.46536814890812067 [0.375,0.578947,0.916667,1,0.619048,0.444444,0.466667,0.714286,0.380952,0.555556,0.555556,0.642857,0.411765,0.173913,0.6,0.153846,0.8125,0.181818,0.3,0.333333,0.529412,0.588235,0.304348,1,0.4375,0.133333,0.166667,0.5,0.75,0.625,0.5625,0.611111,0.470588,0.190476,0.384615,0.666667,0.416667,0.1875,0.7,0.75,0.411765,0.357143,0.666667,0.470588,0.6875,0.315789,0.727273,0.416667,0.157895,0.588235,0.4,0.285714,0.4,0.166667,0.533333,0.058824,0.941176,0.45,0.294118,0.266667,0.875,0.375,0.375,0.428571,0.666667,0.388889,0.125,0.8,0.2,0.157895,0.086957,0.625,0.388889,0.6,0.125,0.555556,0.642857,0.090909,0.9,0.6,0.692308,0.272727,0.428571,0.2,0.32,0.692308,0.5,0.181818,0.375,0.409091,0.631579,0.785714,0.538462,0.1875,0.583333,0.5,0.375,0.368421,0.2,0.5]
predictive_accuracy
0.455
prior_entropy
6.6438561897747395
recall
0.455 [0.375,0.6875,0.6875,0.625,0.8125,0.25,0.4375,0.625,0.5,0.625,0.625,0.5625,0.4375,0.25,0.75,0.125,0.8125,0.25,0.375,0.25,0.5625,0.625,0.4375,1,0.4375,0.125,0.125,0.5,0.5625,0.625,0.5625,0.6875,0.5,0.25,0.625,0.625,0.3125,0.1875,0.875,0.75,0.4375,0.625,0.75,0.5,0.6875,0.375,0.5,0.3125,0.1875,0.625,0.625,0.25,0.375,0.125,0.5,0.0625,1,0.5625,0.3125,0.25,0.4375,0.375,0.5625,0.1875,0.625,0.4375,0.125,0.75,0.25,0.1875,0.125,0.625,0.4375,0.5625,0.0625,0.625,0.5625,0.0625,0.5625,0.5625,0.5625,0.1875,0.375,0.1875,0.5,0.5625,0.3125,0.125,0.1875,0.5625,0.75,0.6875,0.4375,0.1875,0.4375,0.625,0.375,0.4375,0.0625,0.3125]
relative_absolute_error
0.6190924588178308
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08938674021212777
root_relative_squared_error
0.8983705409593956
total_cost
0
area_under_roc_curve
0.8349227022131993 [1,0.988924,1,1,1,0.996855,0.745253,1,0.998418,1,0.996835,0.745253,0.738924,0.734177,1,0.955975,0.996835,0.731013,0.487342,0.481132,0.996855,0.481132,0.987421,1,0.493711,0.490506,0.487342,0.990506,0.746835,0.5,1,1,0.743671,0.731013,1,0.487421,0.974843,0.487421,1,1,1,0.496855,1,0.996835,0.996835,0.990506,1,1,0.474684,0.734177,0.742089,0.740506,0.984277,0.727848,0.996855,0.993711,1,1,0.974843,0.731013,0.496855,0.996855,0.748418,0.493671,1,0.993671,0.995253,1,0.474843,0.977987,0.716772,1,0.746835,0.738924,0.465409,0.996835,0.998418,0.976266,0.746835,0.996835,1,0.493671,0.977987,0.996855,0.737342,0.746835,0.971519,0.726266,1,0.484277,0.740506,1,0.996835,0.984177,0.990506,0.737342,0.996855,0.493671,0.462025,0.996855]
area_under_roc_curve
0.7956831910277842 [1,1,0.746835,1,0.996835,0.993711,0.742089,0.996855,0.746835,1,0.75,0.740506,0.490506,0.743671,1,0.996855,1,0.484177,0.474684,0.990566,1,0.977987,0.987421,1,1,0.721519,0.72943,0.490506,0.996835,1,0.743671,1,0.996835,0.735759,0.727848,1,0.471698,0.490566,0.993711,1,0.490566,0.996855,1,0.738924,0.745253,0.737342,0.993711,0.981132,0.474684,1,0.712025,0.452532,0.987421,0.977848,1,0.477987,1,0.477987,0.993711,0.735759,1,0.993711,0.75,0.496835,1,0.974684,0.742089,0.993711,0.474843,0.987421,0.726266,1,0.743671,0.998418,0.45283,0.996835,0.72943,0.724684,0.993671,0.75,0.743671,0.740506,0.468553,0.474843,0.735759,0.740506,0.738924,0.468354,0.745253,0.496855,0.746835,1,0.97943,0.727848,0.745253,0.992089,0.996855,0.490506,0.735759,0.490566]
area_under_roc_curve
0.8099591493511662 [0.738924,0.745253,0.735759,1,0.743671,0.977848,0.493711,1,0.995253,0.745253,0.743671,0.748418,1,0.746835,0.481013,0.734177,0.996835,0.710443,0.481132,0.481013,1,0.974843,0.721519,1,0.493711,0.996835,1,0.996855,1,0.996835,1,0.990506,0.745253,0.713608,0.487421,0.996855,0.996855,0.490566,1,1,0.493671,0.993711,1,0.996855,0.737342,0.474843,0.746835,0.487421,0.740506,0.746835,0.987342,0.996855,0.990566,0.477848,1,0.737342,1,0.988924,0.993711,0.484177,0.496855,0.496855,0.990506,0.996855,0.987342,0.745253,0.75,1,0.487421,0.477848,0.734177,0.996835,0.484277,0.742089,0.477987,1,0.996835,0.481013,0.734177,0.996835,0.484277,0.496835,0.996835,0.993671,1,1,1,0.996835,0.746835,0.996855,0.990506,1,0.484277,0.735759,0.998418,0.993711,0.735759,0.985759,0.487421,0.740506]
area_under_roc_curve
0.8170856221638406 [0.740506,0.998418,1,0.996855,0.748418,0.490506,0.993711,0.984177,0.746835,0.743671,0.71519,0.992089,0.493711,0.987342,0.996835,0.995253,0.746835,0.985759,0.474843,0.474684,0.968553,1,0.75,1,0.490566,0.727848,0.743671,0.996855,0.992089,0.746835,0.996855,0.496835,0.745253,0.734177,0.996855,0.496855,0.996855,0.974843,0.996855,1,0.743671,0.990566,1,0.987421,1,0.996855,0.992089,1,0.968354,0.990506,0.996835,1,0.996855,0.468354,0.996855,0.72943,1,0.998418,1,0.982595,0.496855,0.996855,0.96519,0.490566,0.746835,1,0.734177,1,0.468553,0.735759,0.477848,0.737342,0.993711,0.990506,0.462264,0.748418,0.75,0.471519,1,0.982595,0.487421,0.487342,1,0.721519,0.968553,0.5,1,0.984177,0.458861,0.984277,1,0.996855,0.471698,0.477848,1,0.5,0.487342,0.996835,0.915094,0.471519]
area_under_roc_curve
0.7809479738874294 [0.742089,1,0.75,1,0.493711,0.746835,0.484277,0.490506,0.490566,0.746835,1,0.993671,1,0.474843,0.993711,0.493671,0.75,0.745253,0.996855,0.993671,0.490506,0.737342,1,1,0.743671,0.46519,0.734177,1,1,0.745253,0.487421,0.490566,0.490506,0.462025,0.493711,1,0.724684,0.477848,0.995253,0.990506,0.992089,0.75,0.5,0.993711,0.996855,0.481132,0.998418,0.5,0.748418,0.988924,1,0.487421,0.998418,0.984277,0.743671,0.699367,0.996855,0.72943,0.993671,0.487421,0.75,0.748418,1,0.496855,0.745253,0.477848,0.477987,1,0.481013,0.710443,0.738924,0.996835,1,0.496855,0.976266,1,0.746835,0.713608,1,0.990566,0.465409,0.996835,0.477848,0.738924,1,0.996855,0.493711,1,1,0.740506,0.996855,1,0.933962,0.987421,0.748418,1,0.734177,0.993711,0.471698,0.734177]
area_under_roc_curve
0.8176591981132073 [0.742089,1,1,1,0.996855,0.993671,0.996855,0.745253,0.993711,0.745253,0.987342,1,0.987421,0.984277,1,0.743671,1,0.987342,0.949686,0.474684,0.75,0.740506,0.75,1,0.734177,0.726266,0.718354,1,1,0.5,1,1,0.735759,0.987342,0.493711,0.996835,0.735759,0.731013,1,0.996835,0.993671,0.740506,1,0.996855,0.996855,0.984277,1,0.5,0.474684,0.745253,0.993711,1,0.982595,0.962264,0.72943,0.484177,1,0.996835,0.993671,0.996855,0.745253,0.735759,0.493711,0.965409,0.743671,0.743671,0.468553,1,0.726266,1,0.477848,0.990506,0.949686,1,0.468354,0.981132,0.743671,0.713608,0.993671,0.493711,0.996855,0.487342,0.737342,0.474684,0.493711,1,0.990566,0.490566,0.484177,0.990506,1,0.987342,0.490566,0.993711,0.743671,0.984277,0.982595,0.484277,0.465409,0.742089]
area_under_roc_curve
0.8281594568505692 [0.985759,0.493711,1,0.742089,1,0.993671,1,1,0.990566,0.740506,1,0.996855,0.740506,0.481132,0.493711,0.738924,0.5,0.481132,0.723101,0.993671,0.742089,0.996835,0.984177,1,0.737342,0.481132,0.962264,0.743671,0.487421,1,0.998418,0.496855,0.996855,0.993711,1,0.75,0.990506,0.743671,0.995253,0.5,0.735759,0.987342,0.743671,0.971519,0.996855,0.738924,0.746835,0.745253,0.993711,1,0.996855,0.738924,1,0.965409,0.743671,0.723101,1,0.740506,0.740506,0.962264,1,0.71519,0.993711,0.493671,0.746835,0.990566,0.471698,0.996835,0.726266,0.468354,0.990566,1,0.732595,1,0.46519,0.990566,1,0.996855,1,0.996855,0.995253,0.993711,0.743671,0.987342,1,0.740506,0.727848,0.481132,0.993711,0.5,0.996855,0.996835,0.735759,0.474843,0.493711,0.745253,0.995253,0.493711,0.731013,0.998418]
area_under_roc_curve
0.8142999661651137 [0.990506,0.993711,1,0.484177,0.996855,0.737342,0.993671,0.996835,0.493711,0.998418,1,0.493711,0.742089,0.981132,0.484277,0.474684,1,0.481132,0.993671,0.490506,0.990506,0.740506,0.742089,1,0.984177,0.459119,0.474843,0.745253,1,0.742089,0.745253,0.996855,1,1,0.740506,1,0.740506,0.988924,0.988924,0.996855,0.996835,0.737342,0.993671,0.490506,1,0.740506,0.998418,1,0.965409,0.5,0.974843,0.481013,0.487342,1,0.743671,0.72943,1,0.996835,0.731013,0.990566,0.5,0.738924,0.993711,0.743671,0.75,0.990566,0.981132,0.75,0.484177,0.735759,0.471698,0.748418,0.996835,0.477987,0.72943,1,0.993711,0.937107,1,0.962264,0.958861,0.981132,0.481013,0.996835,0.46519,0.740506,0.990506,0.490566,0.987421,0.740506,1,0.996835,0.738924,0.974843,0.481132,1,0.981013,0.990566,0.971519,0.72943]
area_under_roc_curve
0.8256902565480452 [0.477987,1,1,1,1,0.996855,0.742089,0.487421,0.737342,1,0.490566,0.996855,0.996835,0.745253,0.996835,0.487421,1,0.981132,1,1,0.998418,1,0.993711,1,0.75,0.449686,0.465409,0.743671,0.742089,1,0.996835,1,0.481132,0.990566,0.993671,1,0.738924,0.735759,1,0.5,0.487421,0.742089,0.75,0.742089,0.490506,0.748418,0.996855,0.490506,0.481132,1,0.743671,0.731013,0.996835,0.719937,0.746835,0.481132,1,0.984277,0.742089,0.72943,0.745253,0.996835,0.734177,0.481013,1,1,0.985759,0.5,0.727848,0.984277,0.984277,0.987421,0.745253,1,0.471519,0.484177,0.996855,0.481132,1,0.996835,1,0.996855,0.481132,0.993711,0.985759,1,0.962025,0.742089,0.465409,0.996835,1,0.75,1,0.471519,1,0.731013,0.949686,0.998418,0.732595,1]
area_under_roc_curve
0.8451116053658148 [0.962264,0.493671,0.5,0.996835,1,0.987421,0.740506,0.481132,0.737342,1,0.996855,0.484277,0.996835,0.737342,0.996835,1,1,1,0.990506,1,1,0.996835,0.496855,1,1,0.459119,1,0.743671,0.487342,0.996855,0.743671,0.75,1,0.974843,0.992089,0.742089,0.726266,0.737342,0.996835,1,0.971698,0.987342,0.75,0.743671,1,0.738924,0.993711,0.481013,0.993711,0.490566,0.995253,0.737342,0.996835,0.724684,0.746835,0.471698,1,0.993711,0.493671,1,1,0.731013,0.487342,0.742089,0.993711,0.5,0.731013,0.996855,0.987342,0.471698,0.468553,0.987421,0.490506,0.996835,0.734177,0.996835,1,0.477987,0.493711,0.996835,1,0.490566,0.996855,0.493711,0.982595,1,0.982595,0.982595,0.455975,0.998418,1,0.996835,0.996835,0.990506,1,1,1,0.743671,1,0.996855]
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
0.4631084441988078
kappa
0.42536901097166313
kappa
0.45685639851582854
kappa
0.43161634103019536
kappa
0.4190314559734775
kappa
0.41907731165397216
kappa
0.42539168870121163
kappa
0.40003947108742843
kappa
0.5261973388083864
kappa
0.5262347506810375
kb_relative_information_score
91.46715177900822
kb_relative_information_score
82.30666409233281
kb_relative_information_score
85.59423084572208
kb_relative_information_score
85.50945181991379
kb_relative_information_score
78.64983648555882
kb_relative_information_score
84.4024127727205
kb_relative_information_score
88.34598092014005
kb_relative_information_score
83.0866100111407
kb_relative_information_score
92.0933138929825
kb_relative_information_score
95.53950665902966
mean_absolute_error
0.01186389998889999
mean_absolute_error
0.012400739364801868
mean_absolute_error
0.012141284583472089
mean_absolute_error
0.012596674506049505
mean_absolute_error
0.012650273511211016
mean_absolute_error
0.012853840603840603
mean_absolute_error
0.012370142010767018
mean_absolute_error
0.013028025620213126
mean_absolute_error
0.011291602841602843
mean_absolute_error
0.011383823815073817
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
0.43125
predictive_accuracy
0.4625
predictive_accuracy
0.4375
predictive_accuracy
0.425
predictive_accuracy
0.425
predictive_accuracy
0.43125
predictive_accuracy
0.40625
predictive_accuracy
0.53125
predictive_accuracy
0.53125
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 [1,0.5,1,1,1,1,0.5,1,0.5,1,1,0.5,0,0,1,0,1,0,0,0,1,0,1,1,0,0,0,0.5,0.5,0,1,1,0.5,0,1,0,0,0,1,1,1,0,1,1,1,0.5,1,1,0,0.5,0.5,0.5,0,0,1,0,1,1,0,0,0,1,0.5,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,1,0.5,0,0.5,0.5,1,0,0,1,0.5,0.5,0,0,0,0,0.5,1,1,0,0,0.5,0,0,0,1]
recall
0.43125 [1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,0.5,0,0.5,1,1,1,0,0,0,1,0,0,1,1,0,0,0,0.5,1,0.5,1,0.5,0.5,0.5,1,0,0,1,1,0,1,1,0.5,0.5,0,0,0,0,1,0,0,1,0.5,1,0,1,0,0,0,1,1,0.5,0,1,0,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,0,0,0,0.5,0,0.5,0,0,0,0.5,1,0.5,0,0.5,0.5,1,0,0,0]
recall
0.4625 [0,0.5,0,1,0.5,0,0,1,1,0.5,0.5,0.5,1,0.5,0,0,1,0,0,0,1,0,0,1,0,1,0,1,1,1,1,1,0.5,0,0,1,1,0,1,1,0,1,1,1,0,0,0.5,0,0.5,0.5,1,1,0,0,1,0.5,1,0.5,1,0,0,0,0.5,1,0.5,0.5,0,1,0,0,0.5,1,0,0.5,0,0,1,0,0,1,0,0,1,0.5,1,0,1,0.5,0.5,1,0.5,1,0,0,0.5,1,0,0.5,0,0.5]
recall
0.4375 [0.5,1,1,0,0.5,0,1,0.5,0,0.5,0,0.5,0,0,1,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0,0.5,0.5,1,0,0.5,0.5,1,0,1,0,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,1,0,0,1,0,1,1,0,0,0,0,1,0,0.5,1,0,1,0,0.5,0,0,1,0.5,0,0.5,0.5,0,1,0.5,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0]
recall
0.425 [0.5,1,0.5,1,0,0.5,0,0,0,0.5,1,0.5,1,0,1,0,0.5,0.5,1,1,0,0.5,1,1,0.5,0,0,1,1,0.5,0,0,0,0,0,1,0.5,0,0.5,1,1,0.5,0,1,1,0,0.5,0,0.5,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0.5,0.5,1,0,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0.5,0,0.5,0,0,1,0,0.5,0,1,0,1,1,0,1,1,0,1,0.5,1,0.5,1,0,0]
recall
0.425 [0,1,1,1,1,0,0,0.5,1,0.5,1,1,0,1,1,0,1,0.5,0,0,0.5,0.5,0,1,0.5,0,0,1,1,0,1,1,0.5,0,0,0,0,0.5,1,0,0.5,0.5,1,1,1,0,1,0,0,0.5,1,1,0,0,0,0,1,1,1,1,0.5,0,0,0,0.5,0.5,0,1,0.5,0.5,0,1,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,1,0,0,0,1,1,0,0,1,0.5,0,0.5,0,0,0.5]
recall
0.43125 [0.5,0,1,0,1,0,1,1,1,0,1,1,0.5,0,0,0,0,0,0,0,0.5,1,0.5,1,0,0,0,0.5,0,1,0.5,0,1,1,1,0.5,0,0.5,1,0,0,1,0.5,0,1,0.5,0,0.5,0,1,1,0,0.5,0,0.5,0,1,0.5,0,0,1,0,1,0,0.5,1,0,1,0.5,0,0,1,0,0,0,1,1,0,1,1,0.5,0,0.5,0,1,0.5,0,0,0,0,1,1,0.5,0,0,0.5,0.5,0,0.5,0.5]
recall
0.40625 [0.5,1,1,0,1,0,0.5,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0.5,0.5,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0.5,0,0.5,1,1,0.5,1,0,1,0.5,0.5,1,0,0,0,0,0,1,0.5,0,1,0.5,0.5,1,0,0.5,1,0.5,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0.5,0,1,0,0,0,1,0.5,0,0,0]
recall
0.53125 [0,1,1,1,1,1,0.5,0,0.5,1,0,1,1,0.5,1,0,1,0,1,1,1,1,1,1,0.5,0,0,0.5,0.5,1,1,1,0,0,1,1,0.5,0,1,0,0,0.5,0.5,0.5,0,0.5,1,0,0,1,0.5,0,1,0,0,0,1,0,0,0,0,1,0.5,0,1,1,0.5,0,0.5,1,1,0,0.5,1,0,0,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,1,1,0,0.5,0,1,0.5,0,1,0,1]
recall
0.53125 [0,0,0,0.5,1,0,0,0,0.5,1,1,0,1,0,1,1,1,1,0.5,1,1,1,0,1,1,0,1,0.5,0,1,0,0.5,1,0,0.5,0.5,0,0.5,1,1,0,0.5,0.5,0.5,1,0.5,0,0,1,0,1,0,1,0,0.5,0,1,1,0,1,1,0,0,0.5,1,0,0,1,0.5,0,0,0,0,1,0.5,1,1,0,0,1,1,0,1,0,1,1,0,0,0,1,1,1,1,0.5,0,1,1,0.5,0,0]
relative_absolute_error
0.5991868681262611
relative_absolute_error
0.6262999679192852
relative_absolute_error
0.613196191084448
relative_absolute_error
0.6361956821237114
relative_absolute_error
0.6389027025864139
relative_absolute_error
0.6491838688808375
relative_absolute_error
0.6247546470084342
relative_absolute_error
0.6579810919299548
relative_absolute_error
0.5702829717981225
relative_absolute_error
0.5749405967208989
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
0.08718611024754971
root_mean_squared_error
0.09086591288798251
root_mean_squared_error
0.09147232997624209
root_mean_squared_error
0.08900833861965611
root_mean_squared_error
0.09279018299671389
root_mean_squared_error
0.09081222333125931
root_mean_squared_error
0.08981021986451022
root_mean_squared_error
0.09184895841459052
root_mean_squared_error
0.08523815469010826
root_mean_squared_error
0.08442548241771673
root_relative_squared_error
0.876253377641461
root_relative_squared_error
0.9132367857047177
root_relative_squared_error
0.9193315067599204
root_relative_squared_error
0.8945674618615126
root_relative_squared_error
0.9325764279651976
root_relative_squared_error
0.9126971853568044
root_relative_squared_error
0.9026266716057678
root_relative_squared_error
0.9231167649883423
root_relative_squared_error
0.8566756876647311
root_relative_squared_error
0.8485080240130699
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
291.38537000017095
usercpu_time_millis
283.2650999998805
usercpu_time_millis
218.9349400005085
usercpu_time_millis
224.33573500075
usercpu_time_millis
220.3425759998936
usercpu_time_millis
215.8680500006085
usercpu_time_millis
217.5858540003901
usercpu_time_millis
220.10264900018228
usercpu_time_millis
220.98229400126002
usercpu_time_millis
223.21117399951618
usercpu_time_millis_testing
1.7861409996839939
usercpu_time_millis_testing
1.7806850000852137
usercpu_time_millis_testing
1.6886590001377044
usercpu_time_millis_testing
1.4171080001688097
usercpu_time_millis_testing
1.4942690004318138
usercpu_time_millis_testing
1.4093480003793957
usercpu_time_millis_testing
1.4049210003577173
usercpu_time_millis_testing
1.4014210000823368
usercpu_time_millis_testing
1.3933770005678525
usercpu_time_millis_testing
1.4011619996381341
usercpu_time_millis_training
289.59922900048696
usercpu_time_millis_training
281.48441499979526
usercpu_time_millis_training
217.2462810003708
usercpu_time_millis_training
222.91862700058118
usercpu_time_millis_training
218.84830699946178
usercpu_time_millis_training
214.4587020002291
usercpu_time_millis_training
216.18093300003238
usercpu_time_millis_training
218.70122800009995
usercpu_time_millis_training
219.58891700069216
usercpu_time_millis_training
221.81001199987804