Compară metode

	K-Nearest Neighbors	Regresia Lasso	Mașina cu Vectori Suport (Clasificare)
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	1967	1996	1995
Autorul original	Cover, T.M. & Hart, P.E.	Tibshirani, R.	Cortes, C. & Vapnik, V.
Tip	Instance-based (non-parametric) learning	Regularized linear regression (L1 penalty)	Maximum-margin classifier (kernel method)
Sursa seminală	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.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Denumiri alternative	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Înrudite	5	4	5
Rezumat	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.