I often talk about explainable AI(XAI) methods and how they can be adapted to address a few pain points that prohibit companies from building and deploying AI solutions. You can check my blog if you need a quick refresher on XAI methods. One such XAI method is Decision Trees. They have gained significant traction historically because of their interpretability and simplicity. However, many think that decision trees cannot be accurate because they look simple, and greedy algorithms like C4.5 and CART don't optimize them well.

Decision tree optimization is notoriously difficult from a computational perspective but essential for the field of interpretable machine learning. Despite efforts over the past 40 years, only recently have optimization breakthroughs been made that have allowed practical algorithms to find optimal decision trees. These new techniques have the potential to trigger a paradigm shift where it is possible to construct sparse decision trees to efficiently optimize a variety of objective functions without relying on greedy splitting and pruning heuristics that often lead to suboptimal solutions. The contribution in this work is to provide a general framework for decision tree optimization that addresses the two significant open problems in the area: treatment of imbalanced data and fully optimizing over continuous variables. We present techniques that produce optimal decision trees over a variety of objectives including F-score, AUC, and partial area under the ROC convex hull. We also introduce a scalable algorithm that produces provably optimal results in the presence of continuous variables and speeds up decision tree construction by several orders of magnitude relative to the state-of-the art.