Automated Feature Engineering and the H2O Feature Store

This lesson walks through automated feature engineering in H2O Driverless AI and how strong features get reused across an organization. It covers how the platform generates candidate features, including interaction terms, polynomial features, and date-based aggregations, then evaluates each one's predictive power and keeps only those that improve performance. It explains that every engineered feature is explainable, showing its construction and importance score, which matters when explaining a model to regulators. It closes by covering how high-value features can be promoted to the H2O Feature Store as versioned, reusable assets.