Run
9861510

Run 9861510

Task 6 (Supervised Classification) letter Uploaded 23-10-2018 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.data.StandardScaler(14)_with_meantrue
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sklearn.preprocessing._encoders.OneHotEncoder(3)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.svm.classes.SVC(22)_C2687.03224536965
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sklearn.svm.classes.SVC(22)_gamma2.6197052788199127
sklearn.svm.classes.SVC(22)_kernel"rbf"
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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)_n_jobsnull
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"
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memorynull
sklearn.feature_selection.variance_threshold.VarianceThreshold(18)_threshold0.0
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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
0.8909 ± 0.0029
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8475 ± 0.0041
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.782 ± 0.0058
Cross-validation details (10-fold Crossvalidation)
KB relative information score
15753.56 ± 11.305
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0161 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.074 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
20000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9511 ± 0.0022
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7905 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
Prior entropy
4.6998
Cross-validation details (10-fold Crossvalidation)
Recall
0.7905 ± 0.0056
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2179 ± 0.0058
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.1923 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.127 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6602 ± 0.0087
Cross-validation details (10-fold Crossvalidation)