Figure 1.
Illustration of the random forest algorithm. The bagging algorithm consists of bootstrapping and aggregating. Each oval represents a bootstrap sample from training data. The bootstrapping is implemented at each tree branching with a different random subset of covariates (Vars) until fit of each tree is optimized. Random forests aggregate “votes” over all trees to estimate classification probabilities