Experienced modelers readily understand the value to be derived from developing multiple models based on population segment splits, rather than a single model for the entire population, in any model development project. Analysis of data and model development on sub-populations reveals unique predictive patterns that build greater precision into the segmented model. However, experienced modelers are also aware of the pitfalls in segmentation analysis. It can be extremely time-consuming, and result in over-fitting—segmentation trees with excessive leaf nodes that may or may not add value in predicting the target of interest. A common response to over-fitting, selecting the best and second-best splits to "grow" the segmentation tree, limits the possibility of finding the most promising initial splits, and splits at each node.