قارن الطرق

	انحدار ريدج (Ridge Regression)	انحدار لاسو	الانحدار اللوجستي
المجال	تعلم الآلة	تعلم الآلة	إحصاء البحث
العائلة	Machine learning	Machine learning	Process / pipeline
سنة النشأة	1970	1996	1958
صاحب الطريقة	Hoerl, A.E. & Kennard, R.W.	Tibshirani, R.	David Roxbee Cox
النوع	L2-regularized linear regression	Regularized linear regression (L1 penalty)	Method
المصدر التأسيسي	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
الأسماء البديلة	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	logit model, binomial logistic regression, LR
ذات صلة	4	4	3
الملخص	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.