There are certain events which are hard to predict since their count of occurrences are very low, however their repercussions on businesses are very high. For instance, medical fraud and waste abuse in the healthcare industry, machine component failures in the automobile industry, occurrence of natural disasters, disease incidents etc. Modeling such rare events using traditional regression methods such as logistic regression impacts the accuracy and stability of the prediction thereby undermining its very purpose.
This whitepaper covers some of the popular techniques that are widely used to model rare events across different lines of businesses. It also gives a brief comparative analysis on the pros and cons of the different techniques thereby ensuring that the end result will produce a significantly more accurate predictive model from a business point of view.Download Now