“One of the potential solutions for model interpretation is to train a surrogate model: a more transparent model that approximates the behavior of the model to be explained. Typically, classification rules or decision trees are used due to their logic-based expressions. However, decision trees can grow too deep, and rule sets can become too large to approximate a complex model. … In this paper, we focus on tabular data and present novel algorithmic and interactive solutions to address these issues.”

Find the paper and full list of authors in IEEE Transactions on Visualization and Computer Graphics