What is feature scaling in Linear Regression?

Updated May 16, 2026

Short answer

Feature scaling standardizes variables to improve optimization performance.

Deep explanation

It ensures features contribute equally to gradient descent and prevents dominance by large-scale variables.

Real-world example

Used in house price models with mixed unit features.

Common mistakes

  • Not scaling before gradient descent.

Follow-up questions

  • Is scaling needed for closed-form solution?

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