Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Geregulariseerde Boosting× | Boosting× | |
|---|---|---|
| Vakgebied | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2001–2016 | 1990–1997 |
| Grondlegger≠ | Friedman, J. H.; extended by Chen & Guestrin | Schapire, R. E.; Freund, Y. |
| Type≠ | Regularized ensemble (boosting with shrinkage/penalty) | Sequential ensemble (iterative reweighting) |
| Oorspronkelijke bron≠ | Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ |
| Aliassen | shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Verwant≠ | 5 | 6 |
| Samenvatting≠ | Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks. | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. |
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