เปรียบเทียบวิธี

	การรวมแบบลงคะแนน	Extra Trees
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	1990s–2004	2006
ผู้ริเริ่ม	Lam & Suen; Kuncheva, L. I. (systematic treatment)	Geurts, P.; Ernst, D.; Wehenkel, L.
ประเภท	Ensemble (combination of multiple classifiers by vote)	Ensemble (extremely randomized decision trees)
แหล่งต้นตำรับ	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
ชื่อเรียกอื่น	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
ที่เกี่ยวข้อง	5	5
สรุป	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.	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.