Feature auditing in a feature store is the process of evaluating and validating the quality, consistency, and performance of features used in a machine learning model.

To perform feature auditing in Databricks, first, create a feature store table with the dataset. Then, use the feature store API to retrieve the features and perform various quality checks, such as data distribution analysis, missing value analysis, and outlier detection. Finally, evaluate the performance of the features using techniques like feature importance and correlation analysis.

Feature auditing in a feature store is not possible as it requires manual feature engineering and analysis.

To perform feature auditing in Databricks, simply list all the features in the dataset and manually check their quality and consistency.