Classification measures and entropy variants can be used as indicators of model fit for logistic regression. These measures rely on a cut-point, "c," to determine predicted group membership. While recommendations exist for determining the location of the cut-point, these methods are primarily anecdotal. The current study used Monte Carlo simulation to compare misclassification rates and entropy variants across four cut-point selection methods: default 0.5, MAXCC, nonevent rate, and MAXSS. Minimal differences were found between methods when group sizes were equal or large between-groups differences were present. The MAXSS method was invariant to group size ratios, however, yielded the highest total misclassification rate and highest amount of misfit. The 0.5 and MAXCC methods are recommended for use in applied research. Recommendations are provided for researchers concerned with small group classification who may use the MAXSS method. "EFR" and "EFR-rescaled" were less influenced by cut-point location than classification methods.