Talking Value: Optimizing Enterprise AI with Profit-Sensitive Scoring
Prove the worth of your machine learning projects in four easy steps.
Getting buy-in on machine learning projects is hard, as is ensuring you’re making the right decision based on your model’s predictions. The best way by far to solve these common problems is to understand what your model is saying in terms of cold, hard cash. But how?
In this whitepaper, our Head of Data Science Services Martin Schmitz will walk you through how to create a confusion matrix to understand your model’s results, and then how to transform this into a matrix that accounts for business gains and losses. This ensures that you’re not just making the best data science decision, but the best business decision.
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