Communicating the results to business users
In real-life scenarios, it is mostly the case that you have to keep communicating with the business intermittently. You might have to build several models before concluding on a final production-ready model and communicate the results to the business.
An implementable model does not always depend on accuracy; you might have to bring in other measures such as sensitivity, specificity, or an ROC curve, and also represent your results through visuals such as a Gain/Lift chart or an output of a K-S test with statistical significance. Note that these techniques require business users' input. This input often guides the way you build the models or set thresholds. Let us look at a few examples to better understand ...
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