Linganisha mbinu

	Kuimarisha	Regularized Decision Tree
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1990–1997	1984
Mwanzilishi	Schapire, R. E.; Freund, Y.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Aina	Sequential ensemble (iterative reweighting)	Supervised learning (regularized tree)
Chanzo asilia	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
Majina mbadala	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Zinazohusiana	6	6
Muhtasari	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.