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Normalization is a fundamental preprocessing step that ensures features are treated fairly in machine learning algorithms. By transforming features to a common range, typically [0, 1], normalization:
- Makes variables with different ranges easier to compare
- Improves the performance of neural networks and distance-based methods
- Prevents bias toward features with larger numeric ranges
- Stabilizes optimization by providing consistent input scales
The key to successful normalization 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.
Choose normalization over standardization when working with neural networks, image data, or when you need to preserve the original distribution shape while ensuring all features contribute equally to the learning process.