Run
10204707

Run 10204707

Task 10093 (Supervised Classification) banknote-authentication Uploaded 17-04-2019 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)(4)Automatically created scikit-learn flow.
sklearn.impute.SimpleImputer(10)_copytrue
sklearn.impute.SimpleImputer(10)_fill_value-1
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sklearn.impute.SimpleImputer(10)_strategy"constant"
sklearn.impute.SimpleImputer(10)_verbose0
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))(4)_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))(4)_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))(4)_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))(4)_transformer_weightsnull
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))(4)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
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sklearn.preprocessing.data.StandardScaler(25)_with_meantrue
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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)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}]
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_learning_rate0.0002350291736313168
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_max_depth25
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_max_features0.8858119589607321
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sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_min_impurity_decrease0.16631271836407746
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sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_min_weight_fraction_leaf0.234415494433372
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_n_estimators1927
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_n_iter_no_change1034
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_presort"auto"
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sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_subsample0.2350207772327363
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_tol0.029130565505554592
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_validation_fraction0.15292104259168926
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(20)_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
0.9649 ± 0.0159
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8897 ± 0.0414
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7762 ± 0.0839
Cross-validation details (10-fold Crossvalidation)
KB relative information score
361.413 ± 2.6041
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3919 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4939 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Number of instances
1372
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8901 ± 0.0416
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8899 ± 0.0413
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9912
Cross-validation details (10-fold Crossvalidation)
Recall
0.8899 ± 0.0413
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7935 ± 0.0146
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4969 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4 ± 0.0084
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8049 ± 0.0169
Cross-validation details (10-fold Crossvalidation)