What is the purpose of feature scaling?
- A Putting features on comparable ranges for distance-based and gradient-descent algorithms
- B Reducing the number of features
- C Removing outliers
- D Balancing classes
Answer
Putting features on comparable ranges for distance-based and gradient-descent algorithms
Tree-based models are scale-invariant, so scaling is unnecessary for them but essential for KNN, SVM and neural networks.





