Run
9938202

Run 9938202

Task 14965 (Supervised Classification) bank-marketing Uploaded 08-01-2019 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,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.Grad ientBoostingClassifier)(3)Automatically created scikit-learn flow.
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)(3)_memorynull
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))(3)_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))(3)_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))(3)_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))(3)_transformer_weightsnull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(3)_memorynull
sklearn.preprocessing.imputation.Imputer(34)_axis0
sklearn.preprocessing.imputation.Imputer(34)_copytrue
sklearn.preprocessing.imputation.Imputer(34)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(34)_strategy"median"
sklearn.preprocessing.imputation.Imputer(34)_verbose0
sklearn.preprocessing.data.StandardScaler(20)_copytrue
sklearn.preprocessing.data.StandardScaler(20)_with_meantrue
sklearn.preprocessing.data.StandardScaler(20)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(3)_memorynull
sklearn.impute.SimpleImputer(6)_copytrue
sklearn.impute.SimpleImputer(6)_fill_value-1
sklearn.impute.SimpleImputer(6)_missing_valuesNaN
sklearn.impute.SimpleImputer(6)_strategy"constant"
sklearn.impute.SimpleImputer(6)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(6)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(6)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(6)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(6)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(6)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(6)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(21)_threshold0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_learning_rate1.7408366377092581
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_max_depth8
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_max_features0.4331642905299742
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_impurity_decrease0.9793990240578202
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_impurity_splitnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_samples_leaf7
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_samples_split2
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_min_weight_fraction_leaf0.07576886218501583
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_n_estimators448
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_n_iter_no_change206
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_random_state50706
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_subsample0.014952517018586686
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_tol1.4560754385970109e-05
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_validation_fraction0.6948508015766471
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(16)_warm_startfalse

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
0.4671 ± 0.1234
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5805 ± 0.2529
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0276 ± 0.1202
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-99047.145 ± 8118.2808
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.5073 ± 0.2855
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
0.78 ± 0.0849
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4927 ± 0.2855
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5207
Cross-validation details (10-fold Crossvalidation)
Recall
0.4927 ± 0.2855
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
2.4553 ± 1.3819
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
0.7122 ± 0.2264
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
2.216 ± 0.7045
Cross-validation details (10-fold Crossvalidation)