Run
10560101

Run 10560101

Task 14952 (Supervised Classification) PhishingWebsites 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_state29372
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
0.9904 ± 0.0012
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9488 ± 0.0057
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8962 ± 0.0116
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8345 ± 0.0092
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.089 ± 0.0045
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4935 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9489 ± 0.0057
Cross-validation details (10-fold Crossvalidation)
Number of instances
11055
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9489 ± 0.0057
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9489 ± 0.0057
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9906 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1803 ± 0.0092
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4967 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1964 ± 0.0067
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3954 ± 0.0134
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9472 ± 0.0059
Cross-validation details (10-fold Crossvalidation)