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Despite a wide array of advanced techniques available today, too many practitioners are forced to return to their old toolkit of approaches deemed “more interpretable.” Whether because of non-legal policy or difficulty in executive presentation, these restraints result from poor analytics communication and inability to explain model risks and outcomes, not a failing of the techniques.
From sampling to feature reduction to supervised modeling, the toolbox and communications of data scientists are limited by these constraints. But, instead of simplifying models, data scientists can re-introduce often ignored statistical practices to describe the models, their risk, and the impact of changes in the customer environment.
Even in situations without restrictions, these approaches will improve how practitioners select models and communicate results. Through measurement and simulation, reviewed approaches can be used to articulate the promises, risks, and assumptions of developed models, without requiring deep statistical explanations.