手法を比較

	ナイーブベイズ	決定木	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	1997	1984	2001
提唱者	Mitchell, T. M. (textbook treatment)	Breiman, Friedman, Olshen & Stone	Breiman, L.
種類	Probabilistic classifier (Bayes' theorem with conditional independence)	Recursive partitioning (if-then rules)	Ensemble (bagging of decision trees)
原典	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
別名	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
関連	4	5	4
概要	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.