Feature Ideation

Data is a finite resource, and you want to get the most out of it. So feature engineering becomes vital for machine learning success. Yet we often lose important signals in our data when our data extracts are limited to the same old boring COUNT and SUM aggregations.

We propose a new approach to feature engineering ideation, based upon a structure using data semantics and signal types, yet with the freedom to apply domain knowledge.