เปรียบเทียบวิธี

	Regularized Decision Tree	Extra Trees
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	1984	2006
ผู้ริเริ่ม	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Geurts, P.; Ernst, D.; Wehenkel, L.
ประเภท	Supervised learning (regularized tree)	Ensemble (extremely randomized decision trees)
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
ที่เกี่ยวข้อง	6	5
สรุป	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.