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. 2019 Apr 24;9(10):5938–5949. doi: 10.1002/ece3.5177

Figure 1.

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