Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uimarishaji wa Mteremko× | Uboreshaji wa Gradient Ulioimarishwa× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2001 | 2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost) |
| Mwanzilishi≠ | Friedman, J. H. | Chen, T. & Guestrin, C. (building on Friedman, J. H.) |
| Aina≠ | Ensemble (sequential boosting of decision trees) | Regularized ensemble (additive tree model) |
| Chanzo asilia≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI ↗ |
| Majina mbadala | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | 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. | Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data. |
| ScholarGateSeti ya data ↗ |
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