Balancing Interpretability and Predictive Power with Cubist Models in R

Machine learning models are powerful tools that do well in their purpose of prediction. In many business applications, the power of these models is quite beneficial. With any application of a machine learning model, the process to choosing which model involves determining the model that performs best across a given set of criteria. One of these criteria is the interpretability of the model. Neural nets to decision trees, to regression models all have varying levels of interpretability.