midSVM
Why is feature scaling important in SVM?
Updated May 17, 2026
Short answer
Feature scaling ensures all features contribute equally to distance calculations.
Deep explanation
SVM relies on distance-based computations; unscaled features can dominate and distort the hyperplane.
Real-world example
Used in image classification pipelines.
Common mistakes
- Training SVM without normalization.
Follow-up questions
- Which scaler is best?
- Does scaling affect kernels?