Comparer des méthodes

	Plus Proches Voisins (PPV)	Régression Lasso
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1967	1996
Auteur d'origine	Cover, T.M. & Hart, P.E.	Tibshirani, R.
Type	Instance-based (non-parametric) learning	Regularized linear regression (L1 penalty)
Source fondatrice	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Alias	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Apparentées	5	4
Résumé	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.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.