Feature engineering, the process creating new input features for machine learning, is one of the most effective ways to improve predictive models.
Coming up with features is difficult, time-consuming, requires expert knowledge. “Applied machine learning” is basically feature engineering. ~ Andrew Ng
Through feature engineering, you can isolate key information, highlight patterns, and bring in domain expertise.
Unsurprisingly, it can be easy to get stuck because feature engineering is so open-ended.
In this guide, we’ll discuss 20 best practices and heuristics that will help you navigate feature engineering.