Run
9632807

Run 9632807

Task 34536 (Supervised Classification) Internet-Advertisements Uploaded 20-10-2018 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.data.StandardScaler(14)_with_meantrue
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sklearn.impute.SimpleImputer(1)_fill_value-1
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sklearn.preprocessing._encoders.OneHotEncoder(3)_n_valuesnull
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sklearn.svm.classes.SVC(22)_C2.674422406891971
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sklearn.svm.classes.SVC(22)_class_weightnull
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
0.8998 ± 0.0259
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9691 ± 0.0079
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8668 ± 0.0354
Cross-validation details (10-fold Crossvalidation)
KB relative information score
2769.4595 ± 11.7647
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0296 ± 0.007
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2409 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Number of instances
3279
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9708 ± 0.0066
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9704 ± 0.007
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5848
Cross-validation details (10-fold Crossvalidation)
Recall
0.9704 ± 0.007
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1228 ± 0.0291
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.347 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.172 ± 0.02
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4957 ± 0.0574
Cross-validation details (10-fold Crossvalidation)