Linganisha mbinu

	Regularized Decision Tree	Miti ya Ziada	Msitu Nasibu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning	Machine learning
Mwaka wa asili	1984	2006	2001
Mwanzilishi	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Geurts, P.; Ernst, D.; Wehenkel, L.	Breiman, L.
Aina	Supervised learning (regularized tree)	Ensemble (extremely randomized decision trees)	Ensemble (bagging of 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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Majina mbadala	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Zinazohusiana	6	5	4
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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.