Run
10560229

Run 10560229

Task 146817 (Supervised Classification) steel-plates-fault Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(gradientboostingclassifier=sklearn.ensemble.gradi ent_boosting.GradientBoostingClassifier)(4)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 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 to None.
sklearn.pipeline.Pipeline(gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}]
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_learning_rate0.05
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_max_depth6
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_max_featuresnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_impurity_split1e-07
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_samples_leaf1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_samples_split2
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_weight_fraction_leaf0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_n_estimators50
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_random_state13658
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_subsample0.5
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_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

0.9438 ± 0.009
Per class
Cross-validation details (10-fold Crossvalidation)
0.7913 ± 0.0262
Per class
Cross-validation details (10-fold Crossvalidation)
0.729 ± 0.034
Cross-validation details (10-fold Crossvalidation)
0.6991 ± 0.0155
Cross-validation details (10-fold Crossvalidation)
0.0903 ± 0.0038
Cross-validation details (10-fold Crossvalidation)
0.2223 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.7919 ± 0.026
Cross-validation details (10-fold Crossvalidation)
1941
Per class
Cross-validation details (10-fold Crossvalidation)
0.795 ± 0.0273
Per class
Cross-validation details (10-fold Crossvalidation)
0.7919 ± 0.026
Cross-validation details (10-fold Crossvalidation)
2.4107 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
0.4063 ± 0.0171
Cross-validation details (10-fold Crossvalidation)
0.3334 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.2034 ± 0.0087
Cross-validation details (10-fold Crossvalidation)
0.6101 ± 0.026
Cross-validation details (10-fold Crossvalidation)
0.7876 ± 0.0236
Cross-validation details (10-fold Crossvalidation)