Linganisha mbinu

	Regularized Decision Tree	Kuimarisha	Miti ya Ziada
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning	Machine learning
Mwaka wa asili	1984	1990–1997	2006
Mwanzilishi	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Schapire, R. E.; Freund, Y.	Geurts, P.; Ernst, D.; Wehenkel, L.
Aina	Supervised learning (regularized tree)	Sequential ensemble (iterative reweighting)	Ensemble (extremely randomized decision trees)
Chanzo asilia	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8	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.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
Majina mbadala	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
Zinazohusiana	6	6	5
Muhtasari	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.	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.	Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time.