قارن الطرق

	شبكة المرونة (Elastic Net)	انحدار لاسو	تحليل المكونات الرئيسية
المجال	تعلم الآلة	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning	Machine learning
سنة النشأة	2005	1996	2002
صاحب الطريقة	Zou, H. & Hastie, T.	Tibshirani, R.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
النوع	Regularized linear regression (L1 + L2 penalty)	Regularized linear regression (L1 penalty)	Unsupervised dimensionality reduction
المصدر التأسيسي	Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.	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.
الأسماء البديلة	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
ذات صلة	4	4	3
الملخص	Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors.	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.