Порівняння методів

	Lasso-регресія	Логістична регресія	Гребенева регресія
Галузь	Машинне навчання	Статистика досліджень	Машинне навчання
Родина	Machine learning	Process / pipeline	Machine learning
Рік появи	1996	1958	1970
Автор методу	Tibshirani, R.	David Roxbee Cox	Hoerl, A.E. & Kennard, R.W.
Тип	Regularized linear regression (L1 penalty)	Method	L2-regularized linear regression
Основоположне джерело	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.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Інші назви	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	logit model, binomial logistic regression, LR	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Пов'язані	4	3	4
Підсумок	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.	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.