We describe a general-purpose classification learning algorithm based on Generalized Linear Models, and evaluate that algorithm (GLOREAL, the Generalized Logistic Regression Algorithmic Learner) as a competitor to ordinary decision tree learners such as C4.5. GLOREAL is based on a generalization of logistic regression and it also uses some non-conventional methods for model selection that are adapted from other work at the interface of machine learning and statistics. Empirical results show that GLOREAL is 4-5% more accurate than C4.5 on the average. GLOREAL's success is due in part to learning and aggregating multiple classifiers for a problem, which is more effective in GLOREAL than it is in bagging decision trees. Another important factor in GLOREAL's success is that it has a richer and more effective representation language than ordinary decision tree learners, particularly with regard to numeric attributes.