Comparer des méthodes

	Régression Lasso	Forêt Aléatoire	Régression Ridge
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1996	2001	1970
Auteur d'origine	Tibshirani, R.	Breiman, L.	Hoerl, A.E. & Kennard, R.W.
Type	Regularized linear regression (L1 penalty)	Ensemble (bagging of decision trees)	L2-regularized linear regression
Source fondatrice	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Apparentées	4	4	4
Résumé	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.