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Automate external data discovery and enrichment for more accurate predictive models.
Congratulations! You’ve spent a considerable amount of time working on getting your data just right and now you’re ready to leave that step behind and focus on your machine learning models. Well, yes and no. You are definitely ready to start focusing on getting insights, but that doesn’t mean you’re done with data. Far from it, actually.
Now it’s time to actually start mining your unified dataset for insights and using it to build models that will give you the best results to your predictive questions.
Part three of our series Explorium Explains: Data for Machine Learning, shines a light on deployment. In this whitepaper we’ll:
Automate external data discovery and enrichment for more accurate predictive models.
It's time to take feature generation - a subset of feature engineering - from an art to a science by opening up additional data sources to achieve breakthroughs in predictive models.
This complete guide breaks down data acquisition into six steps, including data provider due diligence and data provider tests to uplift your model's accuracy.
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