方法对比

	K-Nearest Neighbors	决策树	朴素贝叶斯 (Naive Bayes) 是一种快速的概率分类器，它应用贝叶斯定理，同时假设特征在给定类别时是条件独立的	随机森林
领域	机器学习	机器学习	机器学习	机器学习
方法族	Machine learning	Machine learning	Machine learning	Machine learning
起源年份	1967	1984	1997	2001
提出者	Cover, T.M. & Hart, P.E.	Breiman, Friedman, Olshen & Stone	Mitchell, T. M. (textbook treatment)	Breiman, L.
类型	Instance-based (non-parametric) learning	Recursive partitioning (if-then rules)	Probabilistic classifier (Bayes' theorem with conditional independence)	Ensemble (bagging of decision trees)
开创性文献	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.	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.
别名	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	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
相关	5	5	4	4
摘要	K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values.	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.