Chunk #63 — The Methods — How Do Ensemble Methods Work? — Random Forests

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An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

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Intuitively speaking, random forests can improve the predictive performance even further as compared to bagging, because the single trees involved in averaging are even more diverse. From a statistical point of view, this can be explained by the theoretical results presented by Breiman (2001a), that the upper bound for the generalization error of an ensemble depends on the correlation between the individual trees, such that a low correlation between the individual trees results in a low upper bound for the error.