This paper presents the generalized mixed effects regression tree (GMERT) method, an extension of the mixed effects regression tree (MERT) methodology designed for continuous outcomes to other types of outcomes (e.g., binary outcomes, counts data, ordered categorical outcomes, and multicategory nominal scale outcomes). This extension uses the penalized quasi-likelihood (PQL) method for the estimation and the expectation-maximization (EM) algorithm for the computation. The simulation results in the binary response case show that, when random effects are present, the proposed generalized mixed effects regression tree method provides substantial improvements over standard classification trees.