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A layman's interpretability of random forests versus classical regression models

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Ashley Graham of Stratasan will cover layman's interpretability of random forests versus classical regression models. Specifically, how can a data scientist best 'sell' a non-technical audience on the implementation of random forest techniques, in the case that such techniques provide more accurate modelling.
Classical regression models are easy to interpret, whereas many machine learning techniques are often discussed as being 'black boxes.' How does a data scientist best navigate conversations with non-technical audiences when it comes to explaining their methodology of choice?

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