Neural Networks as Decision Trees

The recent boom in AI has clearly shown the power of deep neural networks in various tasks, especially in the field of classification problems where the data is high-dimensional and has complex, non-linear relationships with the target variables. However, explaining the decisions of any neural classifier is an incredibly hard problem. While many post-hoc methods such as DeepLift [2] and Layer-Wise Relevance Propagation [3] can help with explaining individual decisions, explaining the global decision mechanisms (or what the model generally looks for) is much more difficult. Because of this, many practitioners in high-stakes fields instead opt for more interpretable models like basic Decision Trees since the decision hierarchy can be clearly visualized and understood by stakeholders. However, basic trees by themselves often do not provide enough accuracy for the task at hand and often ensemble methods like Bagging or Boosting are used to improve the model's performance.