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Automate external data discovery and enrichment for more accurate predictive models.
It’s easy to get caught up in a rush to start building your machine learning models. But, before you actually build them, you need to understand your goals and how to achieve them. That process begins with data.
The question is, do you have enough data to get the insights you need? And if not, how can you fill the gaps?
In part one of our new whitepaper series, Explorium Explains: Data for Machine Learning, you’ll learn:
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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