We consider the problem of choosing split points for continuous predictor variables in a decision tree. Previous approaches to this problem typically either (1) discretize the continuous predictor values prior to learning or (2) apply a dynamic method that considers all possible split points for each potential split. In this paper, we describe a number of alternative approaches that generate a small number of candidate split points dynamically with little overhead. We argue that these approaches are preferable to pre-discretization, and provide experimental evidence that they yield probabilistic decision trees with the same prediction accuracy as the traditional dynamic approach. Furthermore, because the time to grow a decision tree is proportional to the number of split points evaluated, our approach is significantly faster than the traditional dynamic approach.