What is feature engineering in supervised learning?
Updated May 17, 2026
Short answer
Feature engineering is the process of creating useful input features to improve model performance.
Deep explanation
Feature engineering transforms raw data into meaningful representations that improve model learning. It includes scaling, encoding categorical variables, handling missing values, and creating derived features that capture domain knowledge.
Real-world example
In fraud detection, creating features like transaction frequency improves accuracy significantly.
Common mistakes
- Ignoring domain knowledge and relying only on raw features.
Follow-up questions
- What is feature scaling?
- What is one-hot encoding?