Normalization
Normalization is a data preprocessing technique that rescales features into a common numerical range (often [0,1]) so that no single variable dominates due to its magnitude.
Background
It is particularly important for algorithms sensitive to feature scales, such as neural networks, k-nearest neighbors, and SVMs. Normalization should not be confused with standardization, which centers data around zero with unit variance.
Examples
- Image processing: pixel values scaled from 0–255 down to [0,1].
- Healthcare: aligning clinical metrics of different units.
- E-commerce: normalizing purchase amounts and frequencies for recommendation systems.
Strengths and challenges
- ✅ Helps stabilize and accelerate training.
- ✅ Improves gradient descent efficiency.
- ❌ Not always needed for tree-based models.
- ❌ Incorrect normalization may distort feature relationships.
Normalization plays a key role in keeping features comparable. When variables exist on very different scales—such as income measured in thousands of dollars and age measured in years—models that rely on distances or weights may become biased toward the larger numbers. By rescaling values to a uniform range, normalization ensures that each feature contributes fairly to the learning process.
It is especially impactful in models where optimization depends on gradient descent, since keeping input values within small ranges helps gradients behave more smoothly and reduces the risk of exploding updates. However, normalization is not a one-size-fits-all solution: for tree-based algorithms like random forests or gradient boosting, feature scaling has little effect because splits are based on thresholds rather than distances.
Different strategies exist, from min–max scaling to more adaptive approaches like robust scaling (which reduces the influence of outliers). Choosing the right method depends on the data distribution and the downstream algorithm. Applied thoughtfully, normalization is a quiet enabler that improves stability, convergence speed, and overall model performance.
📚 Further Reading
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning.
