Interpreting machine learning models – Towards Data Science

Regardless of the end goal of your data science solutions, an end-user will always prefer solutions that are interpretable and understandable. Moreover, as a data scientist you will always benefit from the interpretability of your model to validate and improve your work. In this blog post I attempt to explain the importance of interpretability in machine learning and discuss some simple actions and frameworks that you can experiment with yourself. In traditional statistics, we construct and verify hypotheses by investigating the data at large. We build models to construct rules that we can incorporate into our mental models of processes.