Classification using Random Forests in Stata and R

Many estimation problems focus on classification of cases (into bins) with tools that aim to identify cases using only a small subset of all possible questions. These tools can be used in diagnoses of disease, identification of advanced or failing students using tests, or classification into poor and nonpoor for the targeting of a means-tested social program. Most popular estimation procedures for generating these tools prioritize minimization of in-sample prediction errors, but the objective in generating such tools is the minimization of out-of-sample prediction errors. We provide a comparison of linear discriminant, discrete choice, and random forest methods, with applications to means-tested social programs. Out-of-sample prediction error is typically minimized by random forest algorithms.