Comparar métodos

	Extra Trees	Agregación por Bootstrap (Bagging)	Gradient Boosting
Campo	Aprendizaje automático	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning	Machine learning
Año de origen	2006	1996	2001
Autor original	Geurts, P.; Ernst, D.; Wehenkel, L.	Breiman, L.	Friedman, J. H.
Tipo	Ensemble (extremely randomized decision trees)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (sequential boosting of decision trees)
Fuente seminal	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alias	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Relacionados	5	5	5
Resumen	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.	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.