Comparar métodos

	K-Nearest Neighbors	Regressão Ridge
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1967	1970
Autor original	Cover, T.M. & Hart, P.E.	Hoerl, A.E. & Kennard, R.W.
Tipo	Instance-based (non-parametric) learning	L2-regularized linear regression
Fonte seminal	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Outros nomes	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.