Une forêt aléatoire est une méthode d’apprentissage d’ensemble qui construit de nombreux arbres de décision et agrège leurs sorties pour la classification ou la régression. En entraînant les arbres avec de l’aléa, par exemple par bagging ou échantillonnage de features, elle réduit la tendance des arbres de décision individuels au surapprentissage.
(Also random decision forest.) An ensemble learning method for classification, regression, and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set.
Random Forest is a machine learning algorithm used for both classification and regression. It's not directly a generative AI model itself, but it's a component that can be used within a larger generative AI system. A random forest consists of multiple decision trees, and its inference is an aggregation of the inferences from these individual trees. For example, in a classification task, each tree "votes" for a class, and the final inference is the class with the most votes For more information, see Decision forest.
A side-by-side comparison of Decision Tree and Random Forest. Understand how the terms differ, when each applies, and what the distinction means for AI governance, system design, or assurance evidence.
A side-by-side comparison of Random Forest and Gradient Boosting. Understand how aggregating many randomized trees differs from sequentially improving weak models to reduce prediction errors.