So sánh phương pháp

	Extra Trees	Rừng ngẫu nhiên
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2006	2001
Người khởi xướng	Geurts, P.; Ernst, D.; Wehenkel, L.	Breiman, L.
Loại	Ensemble (extremely randomized decision trees)	Ensemble (bagging of decision trees)
Công trình gốc	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.
Tên gọi khác	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Liên quan	5	4
Tóm tắt	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.