方法对比

	Boosting	决策树	极端随机树 (Extra Trees)
领域	机器学习	机器学习	机器学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	1990–1997	1984	2006
提出者	Schapire, R. E.; Freund, Y.	Breiman, Friedman, Olshen & Stone	Geurts, P.; Ernst, D.; Wehenkel, L.
类型	Sequential ensemble (iterative reweighting)	Recursive partitioning (if-then rules)	Ensemble (extremely randomized decision trees)
开创性文献	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
别名	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
相关	6	5	5
摘要	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.