Salīdzināt metodes

	Elastic Net	Logistiskā regresija	Random Forest	Regulētā lineārā regresija (Ridge Regression)
Nozare	Mašīnmācīšanās	Pētniecības statistika	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Process / pipeline	Machine learning	Machine learning
Izcelsmes gads	2005	1958	2001	1970
Autors	Zou, H. & Hastie, T.	David Roxbee Cox	Breiman, L.	Hoerl, A.E. & Kennard, R.W.
Tips	Regularized linear regression (L1 + L2 penalty)	Method	Ensemble (bagging of decision trees)	L2-regularized linear regression
Pirmavots	Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	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.
Citi nosaukumi	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Saistītās	4	3	4	4
Kopsavilkums	Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors.	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.	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.