Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Gradient Boosting× | LightGBM× | Arbore de decizie regularizat× | |
|---|---|---|---|
| Domeniu | Învățare automată | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning | Machine learning |
| Anul apariției≠ | 2001 | 2017 | 1984 |
| Autorul original≠ | Friedman, J. H. | Ke, G. et al. (Microsoft) | Breiman, L., Friedman, J., Olshen, R., & Stone, C. |
| Tip≠ | Ensemble (sequential boosting of decision trees) | Gradient boosting decision tree ensemble | Supervised learning (regularized tree) |
| Sursa seminală≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154. link ↗ | Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8 |
| Denumiri alternative | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART |
| Înrudite≠ | 5 | 5 | 6 |
| Rezumat≠ | 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. | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. | A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees. |
| ScholarGateSet de date ↗ |
|
|
|