Võrdle meetodeid

	Boosting	LightGBM	Regulaarne otsustuspuu
Valdkond	Masinõpe	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	1990–1997	2017	1984
Looja	Schapire, R. E.; Freund, Y.	Ke, G. et al. (Microsoft)	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Tüüp	Sequential ensemble (iterative reweighting)	Gradient boosting decision tree ensemble	Supervised learning (regularized tree)
Algallikas	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.	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.	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
Rööpnimetused	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Seotud	6	5	6
Kokkuvõte	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.	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.