Run
10560014

Run 10560014

Task 9976 (Supervised Classification) madelon Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)Gradient Boosting for classification. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage ``n_classes_`` regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_learning_rate0.1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_max_depth3
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_max_featuresnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_min_impurity_split1e-07
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_min_samples_leaf1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_min_samples_split2
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_min_weight_fraction_leaf0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_n_estimators100
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_random_state48257
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_subsample1.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(25)_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

0.8191 ± 0.0241
Per class
Cross-validation details (10-fold Crossvalidation)
0.7359 ± 0.0275
Per class
Cross-validation details (10-fold Crossvalidation)
0.4723 ± 0.0551
Cross-validation details (10-fold Crossvalidation)
0.2686 ± 0.0229
Cross-validation details (10-fold Crossvalidation)
0.383 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)
0.7362 ± 0.0276
Cross-validation details (10-fold Crossvalidation)
2600
Per class
Cross-validation details (10-fold Crossvalidation)
0.7371 ± 0.0285
Per class
Cross-validation details (10-fold Crossvalidation)
0.7362 ± 0.0276
Cross-validation details (10-fold Crossvalidation)
1
Cross-validation details (10-fold Crossvalidation)
0.766 ± 0.0197
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)
0.4216 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
0.8432 ± 0.0199
Cross-validation details (10-fold Crossvalidation)
0.7362 ± 0.0276
Cross-validation details (10-fold Crossvalidation)