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What is the purpose of feature scaling?

  1. A Putting features on comparable ranges for distance-based and gradient-descent algorithms
  2. B Reducing the number of features
  3. C Removing outliers
  4. 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.

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