方法对比

	K-Nearest Neighbors	随机森林
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	1967	2001
提出者	Cover, T.M. & Hart, P.E.	Breiman, L.
类型	Instance-based (non-parametric) learning	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. (2001). Random Forests. Machine Learning, 45, 5–32.
别名	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
相关	5	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.	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.