Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery

Figure 2 Illustration of the random forest built from a collection of trees. The random forest adapted the bootstrap sampling method. All participants were randomly selected from the dataset to form the N subset. By forcing each split to consider only a subset of predictors, all predictors were allowed a chance to reveal their importance.