Run
9838001

Run 9838001

Task 21 (Supervised Classification) car Uploaded 22-10-2018 by Scikit-learn Bot
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.pr eprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.St andardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.imput e.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder )),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceT hreshold,svc=sklearn.svm.classes.SVC)(1)Automatically created scikit-learn flow.
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sklearn.preprocessing._encoders.OneHotEncoder(3)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.svm.classes.SVC(22)_C716.6476534597097
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sklearn.svm.classes.SVC(22)_coef00.5061040164602193
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sklearn.svm.classes.SVC(22)_gamma0.01564367454670762
sklearn.svm.classes.SVC(22)_kernel"sigmoid"
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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))(1)_remainder"passthrough"
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))(1)_sparse_threshold0.3
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memorynull
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Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

17 Evaluation measures

Area under ROC curve
0.8697 ± 0.0288
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8762 ± 0.0242
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7291 ± 0.051
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1248.6072 ± 8.8066
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0616 ± 0.0115
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.229 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Number of instances
1728
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8757 ± 0.0249
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8767 ± 0.023
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.2099
Cross-validation details (10-fold Crossvalidation)
Recall
0.8767 ± 0.023
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2692 ± 0.0499
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3381 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2483 ± 0.0239
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7342 ± 0.07
Cross-validation details (10-fold Crossvalidation)