Random forest
Ensemble classifier that aggregates many decorrelated decision trees trained on bootstrap samples with random feature subsets at each split.
A random forest is an ensemble method in supervised learning that builds many decision trees and combines their predictions by majority vote or averaging. Leo Breiman published the algorithm in 2001; each tree is trained on a bootstrap sample of the data through bagging, and at every split only a random subset of features is considered. That injected randomness decorrelates the trees so their individual errors tend to cancel when aggregated.
Random forests often deliver strong accuracy on tabular data with little feature engineering, and they remain widely used in industry alongside gradient-boosted trees. They sit in the classical machine learning tradition that flourished before large-scale deep learning dominated vision and language. See the article and timeline entry.