juniorFeature Engineering
What is normalization and why is it required?
Updated May 16, 2026
Short answer
Normalization scales numerical features into a standard range, usually 0 to 1.
Deep explanation
Normalization ensures that features with different scales do not bias machine learning models. It is especially important for distance-based algorithms like KNN and gradient-based models.
Real-world example
In recommendation systems, normalization prevents high-value features from dominating similarity calculations.
Common mistakes
- Applying normalization after splitting incorrectly or ignoring outliers.
Follow-up questions
- What is difference between normalization and standardization?
- When should normalization not be used?