Сравнение методов

	Регуляризованное дерево решений	Регуляризованная линейная регрессия
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1984	1970–2005
Автор метода	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
Тип	Supervised learning (regularized tree)	Penalized linear model
Основополагающий источник	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Другие названия	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
Связанные	6	4
Сводка	A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees.	Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.