Methoden vergleichen

	Extra Trees	Gradient Boosting
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2006	2001
Urheber	Geurts, P.; Ernst, D.; Wehenkel, L.	Friedman, J. H.
Typ	Ensemble (extremely randomized decision trees)	Ensemble (sequential boosting of decision trees)
Wegweisende Quelle	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.
Aliasnamen	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Verwandt	5	5
Zusammenfassung	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.