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 and use it effectively.
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 white paper breaks down:
- Challenges in feature generation
- Ways to achieve new breakthroughs in your predictive models by tapping into additional data sources
