Run
9754169

Run 9754169

Task 9985 (Supervised Classification) first-order-theorem-proving Uploaded 21-10-2018 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)_sparsetrue
sklearn.svm.classes.SVC(22)_C5.251862344528959
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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"
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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
0.6383 ± 0.0095
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.468 ± 0.0117
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2862 ± 0.0176
Cross-validation details (10-fold Crossvalidation)
KB relative information score
2060.0053 ± 7.0343
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1612 ± 0.0038
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2508 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Number of instances
6118
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.4872 ± 0.0142
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5163 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.3005
Cross-validation details (10-fold Crossvalidation)
Recall
0.5163 ± 0.0113
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6429 ± 0.0149
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3541 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4015 ± 0.0047
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.134 ± 0.013
Cross-validation details (10-fold Crossvalidation)