What determines the accuracy of predictive models?
The accuracy of a predictive model is influenced most strongly by the quality of the data and the freshness of the model. Without good data, it is simply wishful thinking to expect a good model. Without updating the model frequently, the model’s performance will decay over time. Accuracy is measured in two basic ways. Models have false positive rates and false negative rates. For example, consider a model predicting credit card fraud. A false positive means that the model predicted fraud when no fraud was present. A false negative means that the model predicted that the transaction was ok when in fact it was fraudulent. In practice, false positive and false negative rates can be relatively high. The role of a good model is to improve a business process by a significant degree not to make flawless predictions. Only journalists and pundits make flawless predictions. Best practice uses separate, specialized software applications for building models (the model producer) and for scoring mod
