Understanding The Accuracy-Interpretability Trade-Off

In today's article we discussed about the trade off between model accuracy and model interpretability in the context of Machine Learning. Less flexible models are more interpretable and thus are more suitable in the inference context where we are mostly interested in understanding the relationship between the inputs and the output. On the other hand, more flexible models are way less interpretable but the results can be more accurate. Depending on the problem we are working on, we may have to pick the model that best serves our use case. We should however have in mind that in most of the cases, we have to find the sweet spot between model accuracy and model interpretability.