手法を比較

	アンサンブル決定木	ブースティング	Extra Trees
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	1996–2000	1990–1997	2006
提唱者	Breiman, L.; Dietterich, T. G.	Schapire, R. E.; Freund, Y.	Geurts, P.; Ernst, D.; Wehenkel, L.
種類	Ensemble (multiple decision trees combined)	Sequential ensemble (iterative reweighting)	Ensemble (extremely randomized decision trees)
原典	Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg.	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.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
別名	decision tree ensemble, ensemble of decision trees, combined decision trees, multiple classifier system (decision trees)	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
関連	6	6	5
概要	Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification and regression tasks.	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.	Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time.