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AI & Machine Learning · Foundational

Feature Scaling

Transforming numeric features to a common range or distribution so no feature dominates due to its units.

Definition

Scaling matters for gradient-based and distance-based algorithms such as neural networks, SVMs, k-NN, k-means and PCA. The two main methods are normalization (min-max to 0-1) and standardization (zero mean, unit variance). Tree-based models are largely insensitive to scaling.

Exam tip

Fit the scaler on training data only and apply it to validation and test data to avoid leakage.

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