পদ্ধতির তুলনা করুন

	নিয়ন্ত্রিত সিদ্ধান্ত বৃক্ষ	নিয়ন্ত্রিত রৈখিক নির্ভরণ (Regularized Linear Regression)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1984	1970–2005
প্রবর্তক	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
ধরন	Supervised learning (regularized tree)	Penalized linear model
মৌলিক উৎস	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.
অপর নাম	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
সম্পর্কিত	6	4
সারসংক্ষেপ	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.