Run
10560163

Run 10560163

Task 9946 (Supervised Classification) wdbc 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_state55292
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

Area under ROC curve
0.985 ± 0.0197
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9594 ± 0.0249
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.913 ± 0.053
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8244 ± 0.0378
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0937 ± 0.0162
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4676 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9596 ± 0.0248
Cross-validation details (10-fold Crossvalidation)
Number of instances
569
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9596 ± 0.0244
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9596 ± 0.0248
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9526 ± 0.0055
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2004 ± 0.0341
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4835 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1794 ± 0.0384
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3711 ± 0.0787
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9534 ± 0.0277
Cross-validation details (10-fold Crossvalidation)