Minimum Feature Engineering (MFE) refers to the initial, essential set of transformations applied to raw data to make it suitable for machine learning models. Determining this foundational processing involves identifying the most impactful features and applying the simplest possible engineering techniques. For instance, it might involve converting categorical variables into numerical representations or normalizing numerical features to a common scale. This preliminary feature preparation focuses on establishing a baseline model.
Employing this streamlined approach offers several advantages. It reduces computational costs by limiting the number of transformations. Further, it often leads to more interpretable models, as the engineered features are less complex. Historically, this practice arose from the need to efficiently process large datasets with limited computational resources. Its continued use stems from the recognition that starting with a solid, basic representation simplifies subsequent model building and tuning.
