Normalization › Introduction
Michael BrenndoerferMay 19, 20251 min read
Machine Learning from ScratchAll chapters Part 3 of 9Foundations
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In real-world datasets, features often have vastly different scales and ranges. For example, a dataset might contain age (0-100), income (thousands to millions), and binary flags (0 or 1). Without normalization, algorithms like neural networks or k-nearest neighbors would be dominated by features with larger numeric ranges, leading to biased results and poor model performance.
Normalization maps each feature to a common scale, typically between 0 and 1, so that:
- All features contribute equally to distance calculations
- Gradient-based optimization converges more efficiently
- Model coefficients become comparable across features
- Neural networks train more effectively