Simpler is better: Lifting interpretability-performance trade-off via automated feature engineering

摘要

Machine learning has proved to generate useful predictive models that can and should support decision makers in many areas. The availability of tools for AutoML makes it possible to quickly create an effective but complex predictive model. However, the complexity of such models is often a major obstacle in applications, especially in terms of high-stake decisions. We are experiencing a growing number of examples where the use of black boxes leads to decisions that are harmful, unfair or simply wrong. In this paper, we show that very often we can simplify complex models without compromising their performance; however, with the benefit of much needed transparency.