Publication record · 18.cifr/2001.breiman.random-forests
18.cifr/2001.breiman.random-forestsRandom forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost, but are more robust with respect to noise.
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Optimal k (features per split) selection remains open. Extensions to class imbalance, survival analysis, and unsupervised random forest clustering were flagged. Theoretical analysis via infinite-forest kernel limits and gradient-boosted variants are natural follow-ups.