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Engineering new, relevant features that move the needle on your predictive models is one of the hardest problems in data science. Finding and integrating external data to test out those features is, arguably, an even bigger challenge.

There’s so much valuable data out there, but almost no way to find it.

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. Want to know how? Read our new white paper, Feature Generation: The Next Frontier of Data Science, to learn:Artboard (1)

  • The differences between feature engineering and feature generation 
  • Challenges in feature generation
  • Ways to achieve new breakthroughs in your predictive models by tapping into additional data sources 

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