A decision tree is a predictive model that recursively partitions the covariate’s space into subspaces such
that each subspace constitutes a basis for a different prediction function. Decision trees can be used for
various learning tasks including classification, regression and survival analysis. Due to their unique
benefits, decision trees have become one of the most powerful and popular approaches in data science.
Decision forest aims to improve the predictive performance of a single decision tree by training multiple
trees and combining their predictions. This paper provides an introduction to the subject by explaining
how a decision forest can be created and when it is most valuable. In addition, we are reviewing some
popular methods for generating the forest, fusion the individual trees’ outputs and thinning large decision
forests.