A random forest is an ensemble learning method that builds many decision trees and aggregates their outputs for classification or regression. By training trees with randomness, such as bagging or feature sampling, it reduces the tendency of individual decision trees to overfit.
(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.