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 whitepaper breaks down:
- Six easy-to-follow steps for data acquisition
- Complete checklist for data provider due diligence
- Data provider tests to uplift your model’s accuracy
