Salīdzināt metodes

	Regularizētā loģistikā regresija	Lineārā diskriminanta analīze (LDA)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Latent structure
Izcelsmes gads	1996–2005	1936
Autors	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)	Fisher, R. A.
Tips	Penalized classification model	Supervised dimensionality reduction and linear classifier
Pirmavots	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
Citi nosaukumi	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis
Saistītās	5	4
Kopsavilkums	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.	Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning.