Lære Selecting the Right Technique | Choosing and Evaluating Techniques

Feature scaling and normalization are essential preprocessing steps — but no single method is always best. The right technique depends on:

The algorithm you use;
The data distribution (shape, spread, correlation);
The goal (training stability, interpretability, or visualization).

Choosing wisely ensures that models train efficiently, converge faster, and behave predictably.

Note

Quick Heuristics:

If your model uses distance metrics (e.g., KNN, K-means, SVMs), scaling is mandatory — otherwise, large-valued features dominate;
Tree-based models (Decision Trees, Random Forests, Gradient Boosting) are scale-invariant — you can skip scaling;
Standardization usually works as a safe default when unsure;
Whitening is powerful but computationally expensive — use it only when feature correlation clearly hurts performance.

A critical mistake in preprocessing pipelines is data leakage — computing scaling parameters (mean, std, min, max) on the entire dataset before splitting into train/test. This causes the model to “see” information from the test set during training.

Correct approach:

scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)

Incorrect approach:

scaler.fit(X)  # fitting on the whole dataset

Always compute scaling parameters only on training data, then apply them to validation/test data.

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