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	Lasso-regression	Analys av huvudkomponenter
Ämnesområde	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning
Ursprungsår	1996	2002
Upphovsperson	Tibshirani, R.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	Regularized linear regression (L1 penalty)	Unsupervised dimensionality reduction
Ursprungskälla	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Alias	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Närliggande	4	3
Sammanfattning	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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.