Confronta i metodi

	Albero decisionale	Naive Bayes	Random Forest
Campo	Apprendimento automatico	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	1984	1997	2001
Ideatore	Breiman, Friedman, Olshen & Stone	Mitchell, T. M. (textbook treatment)	Breiman, L.
Tipo	Recursive partitioning (if-then rules)	Probabilistic classifier (Bayes' theorem with conditional independence)	Ensemble (bagging of decision trees)
Fonte seminale	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Correlati	5	4	4
Sintesi	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.	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.