Krahasoni metodat

	Regresioni logjistik i rregulluar	Regresioni linear i rregulluar
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	1996–2005	1970–2005
Krijuesi	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
Lloji	Penalized classification model	Penalized linear model
Burimi themelues	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Emërtime të tjera	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
Të lidhura	5	4
Përmbledhja	Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.	Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.