手法を比較

	バギング（ブートストラップ集約）	ナイーブベイズ	ランダムフォレスト
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	1997	2001
提唱者	Breiman, L.	Mitchell, T. M. (textbook treatment)	Breiman, L.
種類	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Probabilistic classifier (Bayes' theorem with conditional independence)	Ensemble (bagging of decision trees)
原典	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
別名	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
関連	5	4	4
概要	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.	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.