Multilayer perceptron neural networks, Gaussian naive Bayes, and logistic regression classifiers were compared when used to make early predictions regarding one-year college student persistence. Two iterations of each model were built, utilizing a grid search process within 10-fold cross-validation in order to tune model parameters for optimal performance on the classification metrics F-Beta and F-1. The results of logistic regression, the historically favored approach in the domain, were compared to the alternative approaches of multilayer perceptron and naive Bayes based primarily on F-Beta and F-1 score performance on a hold-out dataset. A single logistic regression model was found to perform optimally on both F-1 and F-Beta. The logistic regression model outperformed all four of the individual alternative models on the evaluation criteria of concern. A majority voting ensemble and two additional ensembles with empirically derived weights were also applied to the hold-out set. The logistic regression model also outperformed all three ensemble models on the scoring metrics of concern. A visualization technique for comparing and summarizing case-level classifier performance was introduced. The features used in the modeling process comprised traditional and non-traditional elements. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]