What is outlier handling in feature engineering?
Updated May 16, 2026
Short answer
Outlier handling involves detecting and treating extreme values that distort model training.
Deep explanation
Outliers can skew distributions and negatively affect model performance. Techniques include clipping, transformation, removal, or using robust models. The choice depends on whether outliers are noise or meaningful signals.
Real-world example
Used in fraud detection where extreme values may indicate suspicious behavior.
Common mistakes
- Automatically removing all outliers without domain understanding.
Follow-up questions
- What is IQR method?
- When should outliers be kept?