Run
10459183

Run 10459183

Task 9981 (Supervised Classification) cnae-9 Uploaded 20-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(esti mator=sklearn.naive_bayes.MultinomialNB),step_1=sklearn.ensemble._hist_grad ient_boosting.gradient_boosting.HistGradientBoostingClassifier)(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.naive_bayes.MultinomialNB(6)_alpha0.00016818589237359367
sklearn.naive_bayes.MultinomialNB(6)_class_priornull
sklearn.naive_bayes.MultinomialNB(6)_fit_priorfalse
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_l2_regularization2.602114658557497e-07
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.09995229638874537
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins170
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth631
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter320
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes779
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf137
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change49
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"f1_weighted"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.11730125576452718
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.3764952681503059
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.MultinomialNB),step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.MultinomialNB),step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.MultinomialNB),step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_verbosefalse

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
0.9899 ± 0.0031
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8914 ± 0.0276
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.876 ± 0.0304
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8728 ± 0.0243
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0348 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8898 ± 0.0271
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8963 ± 0.0262
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8898 ± 0.0271
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1764 ± 0.0264
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1367 ± 0.0165
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.435 ± 0.0526
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8898 ± 0.0271
Cross-validation details (10-fold Crossvalidation)