विधियों की तुलना करें

	के-निकटतम पड़ोसी (K-Nearest Neighbors)	लासो रिग्रेशन	सपोर्ट वेक्टर मशीन (वर्गीकरण)
क्षेत्र	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	1967	1996	1995
प्रवर्तक	Cover, T.M. & Hart, P.E.	Tibshirani, R.	Cortes, C. & Vapnik, V.
प्रकार	Instance-based (non-parametric) learning	Regularized linear regression (L1 penalty)	Maximum-margin classifier (kernel method)
मौलिक स्रोत	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.
उपनाम	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
संबंधित	5	4	5
सारांश	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.