Interpretability vs. accuracy tradeoff
A hot topic in Data Science these days is the need for interpretability in models: the idea that, in some cases, we should give up some performance in order to be able to understand and explain exactly what the model is doing. One of the obstacles with this approach is the difficulty to define or measure interpretability. That is why, in this article, the authors propose to model the act of enforcing interpretability instead, so we can measure its statistical impacts.

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