Run
9882173

Run 9882173

Task 43 (Supervised Classification) spambase Uploaded 07-12-2018 by Jan van Rijn
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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,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.Grad ientBoostingClassifier)(2)Automatically created scikit-learn flow.
sklearn.preprocessing.imputation.Imputer(29)_axis0
sklearn.preprocessing.imputation.Imputer(29)_copytrue
sklearn.preprocessing.imputation.Imputer(29)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(29)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(29)_verbose0
sklearn.preprocessing.data.StandardScaler(14)_copytrue
sklearn.preprocessing.data.StandardScaler(14)_with_meantrue
sklearn.preprocessing.data.StandardScaler(14)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
sklearn.impute.SimpleImputer(1)_copytrue
sklearn.impute.SimpleImputer(1)_fill_value-1
sklearn.impute.SimpleImputer(1)_missing_valuesNaN
sklearn.impute.SimpleImputer(1)_strategy"constant"
sklearn.impute.SimpleImputer(1)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(3)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(3)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(3)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_learning_rate0.250312885831316
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_max_depth1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_max_features0.460246793950678
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_impurity_decrease0.4714649515344014
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_impurity_splitnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_samples_leaf4
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_samples_split2
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_weight_fraction_leaf0.35251222402702925
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_n_estimators127
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_n_iter_no_change435
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_random_state1677
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_subsample0.8155032882673671
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_tol0.0007900527258007334
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_validation_fraction0.8383674028351525
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_warm_startfalse
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"
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
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)_transformer_weightsnull
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,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(2)_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
0.9135 ± 0.0185
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8449 ± 0.021
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6727 ± 0.0447
Cross-validation details (10-fold Crossvalidation)
KB relative information score
2621.9805 ± 16.7401
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.216 ± 0.0161
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4776 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Number of instances
4601
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8466 ± 0.0206
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8468 ± 0.0204
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9674
Cross-validation details (10-fold Crossvalidation)
Recall
0.8468 ± 0.0204
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4523 ± 0.0338
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4886 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3305 ± 0.0202
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6763 ± 0.0414
Cross-validation details (10-fold Crossvalidation)