We are pleased to share with you our FAQctuary 2020-2 "Features with flat partial dependence plots: not important?"
This paper focuses on partial dependence plots which are often used when modeling with machine learning techniques in order to better understand the effects of the features on the conditional expectation of the response variable. However, these plots must be interpreted with caution. Indeed, they can easily lead to wrong interpretations in case the analyst is not enough familiar with these plots. A typical situation is the case where a feature is important because of its interactions with others while its partial dependence plot is flat. In such a case, an analyst who would only base his analysis on this plot could be tempted to conclude that the feature is not important to explain the conditional expectation of the response while he would be wrong. In this FAQctuary, we aim to illustrate such a situation with the help of a simulated example that is very simple.