Measuring Predictive Power
This chapter explains common methods in evaluating model predictive power. If the goal is defined as finding the most important/risky customers, there are many different ways using the available resources. Analysts measure accuracy and look for answers. It is obvious that two different analysts would provide different models; however, what both are looking for is an adequate level of accuracy. That means that analysts have freedom while looking for models, but the final model needs to be accurate and usable for decision making. No matter what the final model is, the most important factors before the final results are confirmed are the model relevance tests. One can, for example, create several models with the same goal but using different methods or methodologies. The one with highest accuracy level is the best one. It is important to point out that models do not have to be based only on one method but can combine several methods at the same time.