Linganisha mbinu

	Regularized Decision Tree	Miti ya Ziada
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1984	2006
Mwanzilishi	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Geurts, P.; Ernst, D.; Wehenkel, L.
Aina	Supervised learning (regularized tree)	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	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	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
Zinazohusiana	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.	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.