Run
9889684

Run 9889684

Task 14965 (Supervised Classification) bank-marketing 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"median"
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_rate0.0585315261587072
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_max_depth9
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_max_features0.6876190453952562
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_impurity_decrease0.8490803090005966
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_impurity_splitnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_samples_leaf3
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_samples_split14
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_min_weight_fraction_leaf0.20410860996392793
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_n_estimators478
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_n_iter_no_change570
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_random_state12121
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_subsample0.08539631736318509
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_tol0.008048782170815607
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(14)_validation_fraction0.07112650910817764
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
0.8572 ± 0.0081
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8518 ± 0.0034
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1679 ± 0.0205
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-1354.4654 ± 83.1473
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1663 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2066 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Number of instances
45211
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8499 ± 0.0059
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8835 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5207
Cross-validation details (10-fold Crossvalidation)
Recall
0.8835 ± 0.0025
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8048 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3214 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2876 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8948 ± 0.0079
Cross-validation details (10-fold Crossvalidation)