手法を比較

	ブースティング	ランダムフォレスト	正則化勾配ブースティング
分野	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	1990–1997	2001	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
提唱者	Schapire, R. E.; Freund, Y.	Breiman, L.	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
種類	Sequential ensemble (iterative reweighting)	Ensemble (bagging of decision trees)	Regularized ensemble (additive tree model)
原典	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	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.
別名	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
関連	6	4	6
概要	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.