Interpretable machine learning models offer understandable reasoning behind their decision-making process, though they may not always match the performance of their black-box counterparts. This trade-off between interpretability and model performance has sparked discussions around the deployment of AI, particularly in critical applications where knowing the rationale of decision-making is essential for trust and accountability. In this study, we conduct a comparative analysis of several black-box and interpretable models, focusing on a specific NLP use case that has received limited attention: inferring ratings from reviews. Through this use case, we explore the intricate relationship between the performance and interpretability of different models. We introduce a quantitative score called Composite Interpretability (CI) to help visualize the trade-off between interpretability and performance, particularly in the case of composite models. Our results indicate that, in general, the learning performance improves as interpretability decreases, but this relationship is not strictly monotonic, and there are instances where interpretable models are more advantageous.

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.