Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Regressione Lineare (ML)× | Gradient Boosting× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 1805–1809 | 2001 |
| Ideatore≠ | Legendre, A.-M. & Gauss, C.F. | Friedman, J. H. |
| Tipo≠ | Supervised regression | Ensemble (sequential boosting of decision trees) |
| Fonte seminale≠ | Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7 | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Alias | ordinary least squares regression, OLS, least squares regression, multiple linear regression | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Correlati | 5 | 5 |
| Sintesi≠ | Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task. | 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. |
| ScholarGateInsieme di dati ↗ |
|
|