Alternative stopping rules to limit tree expansion for random forest models.

ABSTRACT: Random forests are a popular type of machine learning model, which are relatively robust to overfitting, unlike some other machine learning models, and adequately capture non-linear relationships between an outcome of interest and multiple independent variables. There are relatively few adjustable hyperparameters in the standard random forest models, among them the minimum size of the terminal nodes on each tree. The usual stopping rule, as proposed by Breiman, stops tree expansion by limiting the size of the parent nodes, so that a node cannot be split if it has less than a specified number of observations. Recently an alternative stopping criterion has been proposed, stopping tree expansion so that all terminal nodes have at least a minimum number of observations. The present paper propo

SUBMITTER: Little MP 

PROVIDER: S-EPMC9448733 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature