Interpretability in an Industrial Context – A Sensitivity Analysis Perspective

Manufacturing production and the design of industrial systems are two examples where interpretability of learning methods enables to grasp how the inputs and outputs of a system are connected, and therefore to improve the system efficiency. Although there is no consensus on a precise definition of interpretability, it is possible to identify several requirements: “simplicity, stability, and accuracy”, rarely all satisfied by existing interpretable methods. In this talk, we will discuss two complementary approaches for designing interpretable algorithms. First, we will focus on designing a robust rule learning model, which is simple and highly predictive thanks to its construction based on random forests. Second, for explaining black-box machine learning models directly, we will  develop come connections between variable importance and sensitivity analysis. The objective here is to use sensitivity analysis as a guide for analyzing available importance measures, and conversely to use machine learning tools for proposing new powerful methods in sensitivity analysis.