Run
10560048

Run 10560048

Task 53 (Supervised Classification) vehicle 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_state44420
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

Area under ROC curve
0.9317 ± 0.0131
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7578 ± 0.0528
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.68 ± 0.0709
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.7013 ± 0.0278
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1363 ± 0.0106
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3748 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.76 ± 0.0531
Cross-validation details (10-fold Crossvalidation)
Number of instances
846
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7559 ± 0.0549
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.76 ± 0.0531
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.9991 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3636 ± 0.0284
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4329 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2714 ± 0.0148
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6269 ± 0.0341
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7628 ± 0.053
Cross-validation details (10-fold Crossvalidation)