Standardization › Summary
Michael BrenndoerferApril 25, 20251 min read
Machine Learning from ScratchAll chapters Part 3 of 9Foundations
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Standardization is a fundamental preprocessing step that ensures features are treated fairly in machine learning algorithms. By transforming features to have a mean of zero and a standard deviation of one, standardization:
- Makes variables with different scales easier to compare
- Improves the performance of distance-based and regularization methods
- Prevents bias toward features with larger numeric values
- Stabilizes optimization by normalizing gradients
The key to successful standardization is proper implementation: always fit the scaler on training data only, then transform both training and test sets using the fitted scaler. This prevents data leakage and ensures realistic model evaluation.