Run
9841049

Run 9841049

Task 3896 (Supervised Classification) ada_agnostic 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.imputation.Imputer(29)_missing_values"NaN"
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sklearn.preprocessing._encoders.OneHotEncoder(3)_n_valuesnull
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sklearn.svm.classes.SVC(22)_C112.60391954106686
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sklearn.svm.classes.SVC(22)_coef00.0
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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
sklearn.feature_selection.variance_threshold.VarianceThreshold(18)_threshold0.0
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,svc=sklearn.svm.classes.SVC)(1)_memorynull

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
0.7047 ± 0.0276
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7899 ± 0.0206
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4262 ± 0.0559
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1751.2318 ± 27.4987
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2056 ± 0.0203
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3732 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
4562
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.787 ± 0.0212
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7944 ± 0.0203
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8085
Cross-validation details (10-fold Crossvalidation)
Recall
0.7944 ± 0.0203
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.551 ± 0.0543
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4319 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4534 ± 0.0223
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0498 ± 0.0513
Cross-validation details (10-fold Crossvalidation)