The balance: Accuracy vs. Interpretability

As more and more number of companies start using data science techniques to drive growth, and as C-Suite increasingly relies on the use of such techniques, understanding the trade-off between Accuracy and Interpretabilitybecomes all the more relevant for analytic success.So what is this trade-off between accuracy and interpretability?.are so rampantly used, and the clients right at the start of the project ask questions such as “Are we going to implement Neural Network algorithms?” So it becomes more important to explain the friction between accuracy and interpretability, and to explain why a certain model is best suited in a particular situation.Below is the representation I typically use to explain the business user the choice of a particular algorithm over the other, and how the selection of the algorithm is related to the use case we are trying to solve, and to the business objective we want to achieve.There are a ton of other algorithms that could fit into this spectrum, but these are the ones that are most relevant to the use cases I typically work on.. More details

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