Run
10559956

Run 10559956

Task 3913 (Supervised Classification) kc2 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_state48497
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
0.7975 ± 0.0935
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8118 ± 0.0521
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3969 ± 0.1667
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2938 ± 0.1163
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2088 ± 0.029
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3266 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8218 ± 0.0495
Cross-validation details (10-fold Crossvalidation)
Number of instances
522
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8075 ± 0.0621
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8218 ± 0.0495
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7318 ± 0.0173
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6392 ± 0.0875
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4037 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3582 ± 0.0453
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8872 ± 0.1075
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6799 ± 0.0823
Cross-validation details (10-fold Crossvalidation)