10115231
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)
8040548
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
13
8783
min_samples_split
9
8783
min_weight_fraction_leaf
0.0
8783
presort
false
8783
random_state
44080
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
21151000
description
https://api.openml.org/data/download/21151000/description.xml
-1
21151001
predictions
https://api.openml.org/data/download/21151001/predictions.arff
area_under_roc_curve
0.8473157354797981 [0.794389,0.92734,0.773457,0.839706,0.866596,0.88954,0.834339,0.92517,0.836115,0.968178,0.803129,0.833984,0.736999,0.788747,0.899108,0.764027,0.966383,0.792732,0.806542,0.817314,0.957939,0.821042,0.761265,0.934659,0.827711,0.776495,0.834458,0.793679,0.899858,0.93383,0.868608,0.899108,0.896031,0.826389,0.893091,0.93097,0.827533,0.759884,0.800722,0.898556,0.764757,0.931877,0.901791,0.890586,0.793186,0.925821,0.900943,0.901042,0.897037,0.802596,0.916943,0.834793,0.892124,0.822345,0.89175,0.814394,0.968158,0.865885,0.924972,0.761738,0.834872,0.900351,0.927162,0.769275,0.869969,0.859908,0.818793,0.885436,0.83797,0.751539,0.808949,0.901199,0.860105,0.798236,0.713088,0.765329,0.900292,0.590515,0.933416,0.859987,0.788056,0.868391,0.692294,0.928149,0.816229,0.85395,0.901851,0.891158,0.831755,0.867069,0.935133,0.898694,0.889796,0.818202,0.82339,0.801452,0.790286,0.858961,0.646405,0.835602]
average_cost
0
f_measure
0.40216923509622865 [0.263158,0.391304,0.545455,0.666667,0.588235,0.2,0.580645,0.421053,0.482759,0.9375,0.4,0.516129,0.352941,0.3,0.451613,0.411765,0.727273,0.37037,0.083333,0,0.4,0.3125,0.216216,0.8,0.324324,0,0,0.216216,0.62069,0.666667,0.461538,0.545455,0.5,0.193548,0.418605,0.545455,0.307692,0.216216,0.344828,0.571429,0.428571,0.666667,0.65,0.294118,0,0.416667,0.625,0.6,0.555556,0.424242,0.235294,0.470588,0.424242,0.190476,0.375,0.148148,0.9375,0.466667,0.342857,0.275862,0.444444,0.611111,0.333333,0.5,0.594595,0.333333,0.228571,0.166667,0.266667,0.108108,0.095238,0.6875,0.457143,0.413793,0.24,0.380952,0.606061,0.064516,0.631579,0.26087,0,0.647059,0.235294,0.363636,0.292683,0.37037,0.727273,0.451613,0.4375,0.564103,0.83871,0.628571,0.428571,0.266667,0.230769,0.5625,0.285714,0.137931,0,0.444444]
kappa
0.41098484848484845
kb_relative_information_score
860.5617395908237
mean_absolute_error
0.01357093467380442
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
0.4090988481056491 [0.227273,0.3,0.529412,0.714286,0.555556,0.214286,0.6,0.363636,0.538462,0.9375,0.368421,0.533333,0.333333,0.75,0.466667,0.388889,0.705882,0.454545,0.125,0,0.294118,0.3125,0.190476,0.736842,0.285714,0,0,0.190476,0.692308,0.6,0.391304,0.529412,0.416667,0.2,0.333333,0.529412,0.26087,0.190476,0.384615,0.526316,0.5,0.647059,0.541667,0.277778,0,0.3125,0.625,0.642857,0.5,0.411765,0.222222,0.444444,0.411765,0.4,0.375,0.181818,0.9375,0.5,0.315789,0.307692,0.545455,0.55,0.357143,0.583333,0.52381,0.3,0.210526,0.25,0.285714,0.095238,0.2,0.6875,0.421053,0.461538,0.333333,0.8,0.588235,0.066667,0.545455,0.428571,0,0.611111,0.222222,0.352941,0.24,0.454545,0.705882,0.466667,0.4375,0.478261,0.866667,0.578947,0.5,0.285714,0.3,0.5625,0.333333,0.153846,0,0.4]
predictive_accuracy
0.416875
prior_entropy
6.6438561897747395
recall
0.416875 [0.3125,0.5625,0.5625,0.625,0.625,0.1875,0.5625,0.5,0.4375,0.9375,0.4375,0.5,0.375,0.1875,0.4375,0.4375,0.75,0.3125,0.0625,0,0.625,0.3125,0.25,0.875,0.375,0,0,0.25,0.5625,0.75,0.5625,0.5625,0.625,0.1875,0.5625,0.5625,0.375,0.25,0.3125,0.625,0.375,0.6875,0.8125,0.3125,0,0.625,0.625,0.5625,0.625,0.4375,0.25,0.5,0.4375,0.125,0.375,0.125,0.9375,0.4375,0.375,0.25,0.375,0.6875,0.3125,0.4375,0.6875,0.375,0.25,0.125,0.25,0.125,0.0625,0.6875,0.5,0.375,0.1875,0.25,0.625,0.0625,0.75,0.1875,0,0.6875,0.25,0.375,0.375,0.3125,0.75,0.4375,0.4375,0.6875,0.8125,0.6875,0.375,0.25,0.1875,0.5625,0.25,0.125,0,0.5]
relative_absolute_error
0.6854007411012322
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.0897855596216927
root_relative_squared_error
0.9023788268401168
total_cost
0
area_under_roc_curve
0.8424692500597084 [0.990566,0.985759,0.745253,1,0.487342,0.990566,0.996835,0.996855,0.487342,0.996855,0.490506,0.490506,0.737342,0.71519,0.984177,0.471698,0.990506,0.471519,0.97943,0.981132,0.968553,0.927673,0.474843,1,0.971698,0.985759,0.732595,0.985759,0.740506,0.984277,0.737342,0.985759,0.738924,0.734177,0.973101,0.471698,1,0.965409,0.993711,0.745253,0.465409,0.996855,0.996835,0.962025,0.974684,0.984177,0.996855,1,1,0.743671,0.996835,0.745253,0.984277,0.731013,0.993711,0.996855,1,1,0.965409,0.993671,0.996855,0.996855,0.992089,0.735759,0.996855,0.737342,1,0.996855,0.959119,0.990566,0.734177,1,0.493671,0.996835,0.90566,1,0.992089,0.471519,0.993671,0.993671,0.727848,0.993671,0.477987,1,0.477848,0.702532,0.490506,0.996835,0.984177,0.484277,0.992089,0.996855,0.977848,0.732595,0.724684,0.743671,0.996855,0.996835,0.477848,0.993711]
area_under_roc_curve
0.8487382821829472 [0.993711,0.993671,0.745253,1,0.985759,0.95283,0.746835,0.993711,0.998418,1,0.993671,0.743671,0.743671,0.738924,0.982595,0.990566,0.75,0.732595,0.713608,0.462264,0.996855,0.459119,0.974843,1,0.955975,0.46519,0.963608,0.471519,1,0.984277,1,1,0.988924,0.738924,0.990506,0.993711,0.493711,0.993711,0.984277,0.996835,1,0.993711,0.996835,0.746835,0.471519,0.993671,1,1,0.990506,1,1,0.743671,0.990566,0.968354,0.481132,0.462264,1,0.996855,0.996855,0.471519,1,0.487421,0.985759,0.726266,1,0.732595,0.724684,0.987421,0.433962,0.943396,0.968354,1,0.97943,0.738924,0.465409,0.740506,0.746835,0.726266,1,0.974684,0.718354,0.735759,0.965409,1,0.737342,0.726266,0.996835,0.977848,0.996835,1,0.748418,1,0.974684,0.996835,0.740506,0.734177,0.465409,0.484177,0.971519,0.996855]
area_under_roc_curve
0.8412239521136848 [0.724684,0.969937,0.737342,0.993711,1,0.984177,0.474843,0.990506,0.735759,1,0.988924,0.987342,0.993711,0.984177,0.745253,0.996835,0.996835,0.471519,0.930818,0.46519,0.993711,0.993711,0.737342,0.993711,0.981132,0.721519,0.952532,0.481132,0.996835,1,0.996855,0.996835,0.713608,0.738924,0.993711,0.993711,0.996855,0.459119,1,0.993671,0.737342,0.990566,1,0.993711,0.732595,0.987421,0.746835,0.996855,0.731013,0.743671,0.982595,1,0.968553,0.977848,0.943396,0.718354,1,0.996835,0.484277,0.737342,0.481132,0.993711,0.992089,0.968553,0.484177,0.484177,0.987342,0.982595,0.993711,0.468354,0.718354,0.996835,0.993711,0.707278,0.449686,0.737342,1,0.719937,0.496835,0.996835,0.484277,1,0.474684,0.987342,0.462264,0.984277,1,0.742089,0.984177,0.993711,1,1,0.465409,0.968354,0.993671,0.996855,0.72943,0.990506,0.449686,0.742089]
area_under_roc_curve
0.8157475270679088 [0.732595,0.984177,1,0.993711,0.996835,0.743671,1,0.740506,0.740506,1,0.737342,0.477848,0.490566,0.734177,0.993671,0.743671,1,0.97943,0.468553,0.732595,0.987421,0.990566,1,0.477987,0.465409,0.954114,0.710443,0.984277,0.731013,1,0.996855,0.487342,0.996835,0.969937,0.477987,0.996855,0.990566,0.465409,0.484277,0.996835,0.971519,1,0.990566,1,0.982595,0.971698,0.745253,0.996855,0.996835,0.481013,0.943038,0.987421,0.484277,0.477848,1,0.968354,1,0.474684,0.993711,0.735759,0.996855,0.5,0.981013,0.487421,0.996835,0.996835,0.696203,0.481013,0.959119,0.487342,0.713608,0.75,0.993711,0.746835,0.977987,0.734177,0.732595,0.474684,0.996835,0.738924,0.974843,0.740506,0.455696,0.992089,0.921384,0.965409,1,0.982595,0.468354,0.996855,1,0.996855,0.996855,0.982595,0.987342,0.996855,0.737342,0.984177,0.443396,0.490506]
area_under_roc_curve
0.8570074287477112 [0.731013,0.471698,0.743671,0.746835,1,0.981013,0.481132,0.974684,0.990566,1,0.732595,0.984177,0.484277,0.471698,0.990566,0.740506,1,0.985759,0.927673,0.969937,0.996835,1,0.992089,1,0.97943,0.719937,0.946203,0.977987,1,0.996835,1,0.474843,0.990506,0.734177,0.987421,0.988924,0.981013,0.724684,0.745253,0.737342,0.993671,1,0.996855,0.471698,0.990566,0.990566,1,0.995253,0.71519,0.996835,0.933962,0.484277,0.998418,0.477987,0.745253,0.71519,0.996855,0.968354,0.996835,0.474843,0.724684,0.993671,0.993711,1,0.993671,1,0.474843,0.955696,0.713608,0.960443,0.976266,0.745253,1,0.996855,0.727848,0.977987,0.990506,0.71519,0.996835,0.993711,1,0.996835,0.46519,0.996835,0.477987,0.474843,1,1,0.993671,0.734177,1,0.496835,0.490566,0.990566,0.477848,0.481132,0.734177,0.971698,0.95283,0.746835]
area_under_roc_curve
0.8750983948332136 [0.471519,0.993711,0.738924,1,0.990566,0.737342,0.996855,0.984177,0.996855,0.75,0.745253,1,0.990566,0.990566,0.490566,0.718354,0.993671,0.976266,0.933962,0.955696,0.992089,0.969937,0.963608,1,0.981013,0.955696,0.96519,0.996855,1,0.743671,0.484277,0.993711,0.743671,0.990506,0.996855,0.748418,0.721519,0.982595,0.731013,0.977848,0.691456,0.993671,0.490566,0.993711,0.949686,0.996855,1,0.738924,0.996835,0.734177,0.974843,0.996855,0.745253,0.993711,0.977848,0.734177,1,0.992089,0.987342,0.996855,0.746835,0.968354,0.987421,1,0.996835,0.977848,0.468553,0.996835,0.955696,0.719937,0.716772,0.977848,1,0.996855,0.72943,0.477987,0.740506,0.455696,0.993671,0.477987,0.993711,0.740506,0.993671,0.993671,0.981132,0.996855,1,0.965409,0.477848,0.990506,1,0.735759,0.990566,0.990566,0.976266,1,0.993671,0.477987,0.959119,0.993671]
area_under_roc_curve
0.8764255433484595 [0.992089,0.981132,0.471698,0.743671,1,0.971519,0.982595,0.735759,0.993711,1,1,0.993711,0.471519,1,0.996855,0.452532,1,0.974843,0.723101,0.974684,0.977848,0.699367,0.487342,0.993671,0.738924,0.987421,0.965409,0.727848,0.977987,1,0.745253,0.990566,1,0.481132,0.990506,1,0.963608,0.996835,0.996835,1,0.490506,0.984177,1,1,0.996855,0.990506,0.742089,0.740506,0.996855,1,0.474843,0.996835,0.996835,0.987421,0.973101,0.981013,0.75,1,0.987342,0.968553,0.974684,1,1,0.731013,0.734177,0.990566,0.996855,0.977848,0.987342,0.727848,0.902516,1,0.732595,0.993711,0.971519,0.465409,0.996855,0.993711,0.990566,0.990566,0.727848,1,0.721519,0.990506,0.990506,0.977848,1,0.984277,0.990566,0.746835,0.996855,0.996835,0.71519,0.455975,0.977987,0.474684,0.993671,0.996855,0.468354,0.993671]
area_under_roc_curve
0.8335314913223473 [1,1,0.996855,0.745253,0.993711,0.746835,1,1,0.484277,1,0.481132,1,0.740506,0.430818,0.493711,1,1,0.474843,0.947785,0.955696,0.984177,0.474684,0.481013,0.996835,0.987342,0.446541,0.993711,1,0.496855,0.732595,0.987342,1,1,0.984277,0.743671,0.998418,0.474684,0.723101,0.737342,0.996855,0.731013,1,0.993671,0.707278,0.481132,0.981013,1,0.981013,0.477987,0.981132,0.977987,0.735759,0.977848,0.993711,0.974684,0.718354,1,0.996835,0.735759,0.443396,0.734177,0.993671,0.987421,0.740506,0.996835,0.996855,0.971698,0.732595,0.716772,0.718354,0.449686,0.996835,0.977848,0.987421,0.471519,1,0.996855,0.446541,0.996855,0.996855,0.724684,0.477987,0.973101,0.981013,0.920886,0.734177,0.990506,0.487421,0.996855,0.996835,0.487421,0.974684,0.996835,0.465409,0.474843,0.743671,0.96519,0.474843,0.458861,0.990506]
area_under_roc_curve
0.833669069938699 [0.987421,0.996835,1,0.742089,0.484177,0.987421,0.484177,0.993711,0.996835,1,0.987421,1,0.740506,0.981013,0.987342,0.465409,1,1,0.481013,0.487421,1,0.995253,0.95283,0.75,0.742089,0.471698,0.443396,0.734177,0.996835,0.996855,0.988924,0.996835,0.981132,0.981132,0.992089,0.984177,0.988924,0.468354,0.996835,0.493711,1,0.738924,0.745253,0.97943,0.735759,0.742089,0.996855,1,0.962264,0.993711,0.995253,0.742089,0.731013,0.734177,0.723101,0.95283,1,0.487421,0.996835,0.72943,0.740506,0.75,0.993671,0.738924,0.490566,0.996855,0.723101,0.993711,0.705696,0.996855,0.949686,0.490566,0.988924,0.484177,0.72943,0.474684,0.996855,0.45283,1,0.745253,0.977848,1,0.477987,0.484277,0.993671,0.982595,0.996835,0.740506,0.481132,0.996835,1,0.996835,1,0.723101,0.959119,0.993671,0.465409,0.984177,0.938291,0.487421]
area_under_roc_curve
0.8495904983679641 [0.468553,0.734177,0.468553,0.745253,1,0.962264,1,0.977987,1,1,0.996855,0.996855,0.987342,0.726266,0.996835,0.968553,0.996855,0.977987,0.971519,1,0.732595,0.71519,0.465409,1,0.487342,0.974843,0.481132,0.712025,0.971519,1,0.745253,1,0.990566,0.984277,0.737342,0.996835,0.743671,0.719937,0.481013,0.996855,0.481132,0.732595,0.740506,0.96519,0.740506,0.737342,0.996855,0.743671,0.996855,0.487421,0.716772,0.993671,0.992089,0.984177,1,0.977987,1,0.490566,0.988924,0.993671,1,1,0.493671,0.734177,0.987421,0.968553,0.969937,0.968553,0.977848,0.95283,0.968553,1,0.737342,0.71519,0.678797,0.97943,1,0.455975,0.996855,0.72943,0.721519,0.993711,0.981132,0.484277,0.993671,0.982595,0.723101,0.968354,1,0.731013,1,0.996835,0.958861,0.716772,0.996855,0.996835,0.459119,0.984177,0.468354,1]
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
0.4002288600402478
kappa
0.46963418965313125
kappa
0.38138635735984533
kappa
0.4004890747022167
kappa
0.40630797773654914
kappa
0.37465455981050133
kappa
0.4820371101460718
kappa
0.3996366221660479
kappa
0.41905438471860446
kappa
0.3749013417521705
kb_relative_information_score
84.29011501339028
kb_relative_information_score
89.11938824464919
kb_relative_information_score
83.72467041779471
kb_relative_information_score
80.18480930345413
kb_relative_information_score
89.31036392112406
kb_relative_information_score
87.68773910890961
kb_relative_information_score
93.57605214353764
kb_relative_information_score
83.41795608289665
kb_relative_information_score
85.25046599021404
kb_relative_information_score
84.00017936486273
mean_absolute_error
0.013860073462511377
mean_absolute_error
0.012931806656110864
mean_absolute_error
0.0139954002738159
mean_absolute_error
0.013706568533725996
mean_absolute_error
0.013345970106469377
mean_absolute_error
0.013918011503236297
mean_absolute_error
0.012926935875484083
mean_absolute_error
0.0137640236353372
mean_absolute_error
0.013388570175094936
mean_absolute_error
0.013871986516259089
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
0.40625
predictive_accuracy
0.475
predictive_accuracy
0.3875
predictive_accuracy
0.40625
predictive_accuracy
0.4125
predictive_accuracy
0.38125
predictive_accuracy
0.4875
predictive_accuracy
0.40625
predictive_accuracy
0.425
predictive_accuracy
0.38125
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
0.40625 [1,0.5,0.5,1,0,0,1,1,0,1,0,0,0,0,0.5,0,0,0,0,0,0,0,0,1,0,0,0,0,0.5,0,0.5,0.5,0.5,0,0.5,0,1,1,1,0.5,0,1,1,0,0,1,1,1,1,0.5,0,0.5,0,0,1,1,1,1,0,1,1,1,0,0.5,1,0,1,0,0,0,0.5,1,0,1,0,1,0.5,0,1,0,0,1,0,1,0,0,0,1,0.5,0,0.5,1,0.5,0.5,0,0.5,1,0,0,1]
recall
0.475 [0,1,0.5,1,0,0,0.5,1,0.5,1,0.5,0.5,0.5,0.5,0.5,1,0.5,0.5,0,0,1,0,0,1,0,0,0,0,1,1,0.5,1,0.5,0.5,0,0,0,1,0,1,1,1,1,0.5,0,1,0,1,0.5,1,1,0.5,1,0,0,0,1,1,1,0,1,0,0.5,0.5,1,0,0,0,0,0,0,1,0.5,0.5,0,0.5,0.5,0.5,1,0,0,0.5,0,1,0.5,0.5,1,0.5,1,1,0.5,1,0,1,0.5,0,0,0,0,1]
recall
0.3875 [0,0,0.5,0,1,0.5,0,0.5,0.5,1,0.5,0.5,1,0.5,0.5,1,1,0,0,0,0,0,0,1,0,0,0,0,0.5,1,1,0,0,0.5,1,1,1,0,0,1,0.5,0,1,1,0,1,0.5,1,0.5,0.5,0.5,1,0,0,0,0,1,1,0,0,0,1,0.5,0,0,0,0.5,0.5,1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0.5,0,1,1,1,0,0,0.5,1,0,0.5,0,0]
recall
0.40625 [0,1,1,1,1,0.5,1,0.5,0,1,0.5,0,0,0,1,0.5,0.5,0.5,0,0,0,1,1,0,0,0,0,1,0.5,1,1,0,1,0,0,1,1,0,0,1,0.5,1,1,1,0,0,0,1,1,0,0,0,0,0,1,0.5,1,0,1,0,1,0,0.5,0,1,1,0,0,0,0,0,0.5,1,0.5,0,0,0,0,1,0.5,0,0.5,0,0.5,0,0,1,0.5,0,1,1,1,1,0,0,1,0,0,0,0]
recall
0.4125 [0.5,0,0.5,0.5,1,0,0,0,0,1,0.5,0,0,0,0,0,1,0.5,0,0,1,1,0.5,1,0.5,0,0,0,1,0.5,1,0,1,0,1,0.5,0.5,0,0.5,0.5,1,1,1,0,0,1,1,0.5,0,0,0,0,0.5,0,0.5,0,1,0,1,0,0,1,1,1,0.5,1,0,0,0,0,0,0.5,1,0,0.5,0,0.5,0,1,0,0,1,0,0.5,0,0,1,1,1,0.5,1,0,0,0,0,0,0.5,0,0,0.5]
recall
0.38125 [0,1,0.5,1,1,0,1,0,0,0.5,0.5,1,0,0,0,0.5,1,0.5,0,0,0.5,0.5,0.5,1,0.5,0,0,0,1,0.5,0,1,0.5,0.5,1,0.5,0,0,0,0,0,1,0,0,0,1,0.5,0,1,0.5,0,0,0.5,1,0,0,1,0.5,0.5,1,0.5,0,0,1,1,0,0,0,0,0,0,0,1,1,0.5,0,0.5,0,0.5,0,0,0.5,1,0.5,0,0,1,0,0,1,1,0,0,1,0.5,1,0,0,0,0.5]
recall
0.4875 [0.5,0,0,0.5,1,0,0.5,0.5,1,1,1,1,0,1,1,0,1,0,0.5,0,1,0,0,1,0.5,0,0,0.5,0,1,0.5,0,1,0,1,1,0,1,0,1,0,0,1,1,0,1,0.5,0.5,1,1,0,1,0.5,0,0.5,0,0.5,1,0,0,0,1,1,0,0.5,1,1,0.5,1,0,0,1,0,1,0.5,0,1,0,1,0,0,1,0.5,0.5,0.5,0.5,1,0,0,0.5,1,1,0,0,0,0,1,0,0,1]
recall
0.40625 [1,1,1,0.5,1,0.5,1,1,0,1,0,1,0.5,0,0,1,1,0,0,0,0.5,0,0,1,1,0,0,0.5,0,0.5,0,1,1,0,0.5,0.5,0,0,0.5,1,0,1,1,0,0,0,1,0,0,0,0,0.5,0.5,0,0,0,1,0,0,0,0,1,0,0.5,1,0,0,0,0,0.5,0,1,0.5,0,0,1,1,0,0,1,0,0,0.5,0,0,0.5,1,0,1,1,0,0.5,1,0,0,0.5,0,0,0,0.5]
recall
0.425 [0,1,1,0.5,0,0,0,1,1,1,0,1,0.5,0,0.5,0,1,1,0,0,1,0,0,0.5,0.5,0,0,0.5,1,1,1,1,0,0,0.5,0,0.5,0,1,0,1,0.5,0.5,0,0,0.5,1,1,0,1,0.5,0.5,0.5,0.5,0,0,1,0,0,0,0,0.5,0,0.5,0,1,0,0,0,1,0,0,0.5,0,0,0,1,0,1,0.5,0,1,0,0,1,0.5,1,0.5,0,1,1,1,1,0,0,1,0,0,0,0]
recall
0.38125 [0,0,0,0.5,1,0,0.5,0,1,1,1,1,1,0,0,0,1,0,0,0,0.5,0.5,0,1,0,0,0,0,0,1,0.5,1,1,0,0.5,1,0.5,0,0,0,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0.5,0,1,0,1,0,0.5,0.5,1,1,0,0.5,1,0,0,0,0.5,0,0,1,0.5,0,0,0,1,0,1,0,0,0,0,0,1,0.5,0,0,1,0,1,1,0,0,0,1,0,0.5,0,1]
relative_absolute_error
0.7000037102278462
relative_absolute_error
0.6531215482884264
relative_absolute_error
0.7068383976674686
relative_absolute_error
0.6922509360467664
relative_absolute_error
0.6740388942661291
relative_absolute_error
0.7029298739008221
relative_absolute_error
0.6528755492668717
relative_absolute_error
0.695152708855413
relative_absolute_error
0.6761904128835815
relative_absolute_error
0.7006053796090437
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
0.09147312709326831
root_mean_squared_error
0.08718754360581198
root_mean_squared_error
0.09090786528453441
root_mean_squared_error
0.09113065022974898
root_mean_squared_error
0.0878469436634081
root_mean_squared_error
0.09067067345987893
root_mean_squared_error
0.08588642511035548
root_mean_squared_error
0.09017910322843249
root_mean_squared_error
0.0904754490016438
root_mean_squared_error
0.09188916670446359
root_relative_squared_error
0.9193395180874657
root_relative_squared_error
0.8762677834340249
root_relative_squared_error
0.9136584231544717
root_relative_squared_error
0.9158974961005826
root_relative_squared_error
0.8828950033667071
root_relative_squared_error
0.9112745556219809
root_relative_squared_error
0.8631910505333554
root_relative_squared_error
0.906334088907387
root_relative_squared_error
0.9093124759920828
root_relative_squared_error
0.9235208735064355
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
135.14236599985452
usercpu_time_millis
120.83008800073003
usercpu_time_millis
123.80645600023854
usercpu_time_millis
94.17053899960592
usercpu_time_millis
93.7218330000178
usercpu_time_millis
91.52808699946036
usercpu_time_millis
92.41838900197763
usercpu_time_millis
93.02429799936363
usercpu_time_millis
91.42164699915156
usercpu_time_millis
91.95027799796662
usercpu_time_millis_testing
1.715956001135055
usercpu_time_millis_testing
1.6929869998421054
usercpu_time_millis_testing
1.6786520009191008
usercpu_time_millis_testing
1.306274998569279
usercpu_time_millis_testing
1.3397639995673671
usercpu_time_millis_testing
1.2454019997676369
usercpu_time_millis_testing
1.3136020006641047
usercpu_time_millis_testing
1.3540630006900756
usercpu_time_millis_testing
1.2410899998940295
usercpu_time_millis_testing
1.288603998546023
usercpu_time_millis_training
133.42640999871946
usercpu_time_millis_training
119.13710100088792
usercpu_time_millis_training
122.12780399931944
usercpu_time_millis_training
92.86426400103664
usercpu_time_millis_training
92.38206900045043
usercpu_time_millis_training
90.28268499969272
usercpu_time_millis_training
91.10478700131353
usercpu_time_millis_training
91.67023499867355
usercpu_time_millis_training
90.18055699925753
usercpu_time_millis_training
90.6616739994206