Investigating feature importance
We have already spent quite some time creating the entire pipeline and tuning the models to achieve better performance. However, what is equally—or in some cases even more—important is the model’s interpretability. That means not only giving an accurate prediction but also being able to explain the why behind it. For example, we can look into the case of customer churn. Knowing what the actual predictors of the customers leaving are might be helpful in improving the overall service and potentially making them stay longer.
In a financial setting, banks often use machine learning in order to predict a customer’s ability to repay credit or a loan. In many cases, they are obliged to justify their reasoning, that is, if they decline a credit application, they need to know exactly why this customer’s application was not approved. In the case of very complicated models, this might be hard, or even impossible.
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