Using Decision Trees to Understand Student Data

We apply and evaluate a decision tree algorithm to university records, producing human-readable graphs that are useful both for predicting graduation, and understanding factors that lead to graduation. We compare this method to that of nueral networks, Support Vector Machines, and Kernel Regression, and show that it is equally powerful as a classificaion tool. At the same time, decision trees provide simple, readable models of graduation that we hope decision-makers will find useful in assessing their programs and understanding their student body.