Comparar métodos

	K-Nearest Neighbors	Random Forest
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1967	2001
Autor original	Cover, T.M. & Hart, P.E.	Breiman, L.
Tipo	Instance-based (non-parametric) learning	Ensemble (bagging of decision trees)
Fonte seminal	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.
Outros nomes	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relacionados	5	4
Resumo	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.