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| Uogólniony model addytywny (GAM)× | Gradient Boosting× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1986 | 2001 |
| Twórca≠ | Trevor Hastie & Robert Tibshirani | Friedman, J. H. |
| Typ≠ | Semi-parametric additive regression model | Ensemble (sequential boosting of decision trees) |
| Źródło pierwotne≠ | Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Inne nazwy | GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response. | 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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