เปรียบเทียบวิธี

	Extra Trees	Bagging (Bootstrap Aggregating)
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2006	1996
ผู้ริเริ่ม	Geurts, P.; Ernst, D.; Wehenkel, L.	Breiman, L.
ประเภท	Ensemble (extremely randomized decision trees)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
แหล่งต้นตำรับ	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
ชื่อเรียกอื่น	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
ที่เกี่ยวข้อง	5	5
สรุป	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.