مقایسهٔ روش‌ها

	درختان اضافی (Extra Trees)	گرادیان بوستینگ (Gradient Boosting)	جنگل تصادفی
حوزه	یادگیری ماشین	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning	Machine learning
سال پیدایش	2006	2001	2001
پدیدآور	Geurts, P.; Ernst, D.; Wehenkel, L.	Friedman, J. H.	Breiman, L.
نوع	Ensemble (extremely randomized decision trees)	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)
منبع بنیادین	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
نام‌های دیگر	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
مرتبط	5	5	4
خلاصه	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.