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The Complete Guide For Data Acquisition

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

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Extend Your Machine Learning Pipeline With Your Prediction Outcome
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Does your machine learning pipeline end after your model makes a prediction? If your answer is “yes,” it do...

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