Feature derivation in a feature store is the process of creating new features from existing ones to improve the performance of a machine learning model.

Feature derivation in a feature store is not possible as it requires manual feature engineering and selection.

To derive new features from existing ones in Databricks, simply list all the features in the dataset and manually create new features based on their relationships.