Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Robust Boosting× | Gradient Boosting× | Robust Gradient Boosting× | |
|---|---|---|---|
| Fagfelt | Maskinlæring | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning | Machine learning |
| Opprinnelsesår≠ | 1999–2001 | 2001 | 2001 |
| Opphavsperson≠ | Freund, Y.; Mason, L. et al. | Friedman, J. H. | Friedman, J. H. (with Huber loss from Huber, P. J.) |
| Type≠ | Ensemble (robust sequential boosting) | Ensemble (sequential boosting of decision trees) | Ensemble (boosted trees with robust loss) |
| Opprinnelig kilde≠ | Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Alias | noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees |
| Relaterte≠ | 6 | 5 | 6 |
| Sammendrag≠ | Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors. | 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. | Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees. |
| ScholarGateDatasett ↗ |
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