Data scientists are constantly challenged with improving their ML models. But when a new algorithm won’t improve your AUC there’s only one place to look: DATA. Generating, testing, and integrating new features from various internal and/or external sources is time-consuming, difficult, and more “artistic.” But it could lead to a major discovery and move the needle much more. This guide walks you through six easy steps for data acquisition, a complete checklist for data provider due diligence, and data provider tests to uplift your model’s accuracy.
