Run
10560808

Run 10560808

Task 31 (Supervised Classification) credit-g Uploaded 13-09-2021 by Victorien Fandos
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Flow

sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transfor mer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotE ncoder),hist=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.His tGradientBoostingClassifier)(1)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.preprocessing._encoders.OneHotEncoder(29)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(29)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(29)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(29)_handle_unknown"error"
sklearn.preprocessing._encoders.OneHotEncoder(29)_sparsetrue
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "OneHotEncoder", "step_name": "OneHotEncoder", "argument_1": ["foreign_worker", "own_telephone", "job", "housing", "other_payment_plans", "property_magnitude", "other_parties", "personal_status", "employment", "savings_status", "purpose", "credit_history", "checking_status"]}}]
sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder),hist=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder),hist=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "ColumnTansformer", "step_name": "ColumnTansformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "hist", "step_name": "hist"}}]
sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder),hist=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_verbosefalse
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_categorical_featuresnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_early_stopping"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_l2_regularization0.3265
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_learning_rate0.5796
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_max_bins255
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_max_depth10
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_max_iter100
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_max_leaf_nodes31
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_min_samples_leaf20
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_monotonic_cstnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_n_iter_no_change10
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_random_state26933
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_scoring"loss"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_tol1e-07
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_validation_fraction0.1
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(8)_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.

18 Evaluation measures

Area under ROC curve
0.763 ± 0.0455
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7338 ± 0.0242
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3504 ± 0.0621
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3347 ± 0.0558
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2624 ± 0.0209
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.743 ± 0.0221
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7307 ± 0.0256
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.743 ± 0.0221
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8813
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6246 ± 0.0497
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4524 ± 0.0258
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9872 ± 0.0563
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.665 ± 0.0322
Cross-validation details (10-fold Crossvalidation)