Run
10560009

Run 10560009

Task 9985 (Supervised Classification) first-order-theorem-proving 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_state50599
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
0.8362 ± 0.007
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5533 ± 0.0105
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4062 ± 0.0136
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3328 ± 0.0074
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1907 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2508 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5798 ± 0.0093
Cross-validation details (10-fold Crossvalidation)
Number of instances
6118
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5555 ± 0.011
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5798 ± 0.0093
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.3 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7603 ± 0.0058
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3541 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3043 ± 0.002
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8593 ± 0.0057
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4359 ± 0.0149
Cross-validation details (10-fold Crossvalidation)