Compară metode

	Pădurea Aleatoare (Random Forest)	Regresia Ridge
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2001	1970
Autorul original	Breiman, L.	Hoerl, A.E. & Kennard, R.W.
Tip	Ensemble (bagging of decision trees)	L2-regularized linear regression
Sursa seminală	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.
Denumiri alternative	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Înrudite	4	4
Rezumat	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.