Сравнение на методи

	Elastic Net	Регресия Ласо	Логистична регресия	Анализ на главните компоненти
Област	Машинно обучение	Машинно обучение	Статистика за изследвания	Машинно обучение
Семейство	Machine learning	Machine learning	Process / pipeline	Machine learning
Година на възникване	2005	1996	1958	2002
Създател	Zou, H. & Hastie, T.	Tibshirani, R.	David Roxbee Cox	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Тип	Regularized linear regression (L1 + L2 penalty)	Regularized linear regression (L1 penalty)	Method	Unsupervised dimensionality reduction
Основополагащ източник	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.	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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Други названия	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	logit model, binomial logistic regression, LR	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Свързани	4	4	3	3
Резюме	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.	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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.