Predictive modeling: Striking a balance between accuracy and interpretability

Editor's note: Register for the free webcast "How the machine learning wave is changing the way organizations look at analytics," hosted by Patrick Hall, senior machine learning scientist at SAS, and Andrew Pease, principal business solutions manager at SAS, to learn how different organizations are finding success with machine learning. The inherent trade-off between accuracy and interpretability in predictive modeling can be a catch-22 for analysts and data scientists working in regulated industries. Professionals in the regulated verticals of banking and insurance often feel locked into using traditional, linear modeling techniques to create their predictive models. This is mainly due to strenuous regulatory and documentation requirements. As machine learning becomes more mainstream, the forces of innovation and competition often drive these same analysts and data scientists to break out of the mold and try new algorithms with more predictive capacity.