Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| XGBoost× | Albero decisionale× | Gradient Boosting× | |
|---|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning | Machine learning |
| Anno di origine≠ | 2016 | 1984 | 2001 |
| Ideatore≠ | Chen, T. & Guestrin, C. | Breiman, Friedman, Olshen & Stone | Friedman, J. H. |
| Tipo≠ | Ensemble (gradient-boosted decision trees) | Recursive partitioning (if-then rules) | Ensemble (sequential boosting of decision trees) |
| Fonte seminale≠ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Alias≠ | XGBoost, extreme gradient boosting, scalable tree boosting | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Correlati | 5 | 5 | 5 |
| Sintesi≠ | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. |
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