Selecting the Right Technique
Feature scaling and normalization are essential preprocessing steps β 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.
Quick Heuristics:
- If your model uses distance metrics (e.g., KNN, K-means, SVMs), scaling is mandatory β otherwise, large-valued features dominate;
- Tree-based models (Decision Trees, Random Forests, Gradient Boosting) are scale-invariant β you can skip scaling;
- Standardization usually works as a safe default when unsure;
- Whitening is powerful but computationally expensive β use it only when feature correlation clearly hurts performance.
A critical mistake in preprocessing pipelines is data leakage β computing scaling parameters (mean, std, min, max) on the entire dataset before splitting into train/test.
This causes the model to βseeβ 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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Feature scaling and normalization are essential preprocessing steps β 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.
Quick Heuristics:
- If your model uses distance metrics (e.g., KNN, K-means, SVMs), scaling is mandatory β otherwise, large-valued features dominate;
- Tree-based models (Decision Trees, Random Forests, Gradient Boosting) are scale-invariant β you can skip scaling;
- Standardization usually works as a safe default when unsure;
- Whitening is powerful but computationally expensive β use it only when feature correlation clearly hurts performance.
A critical mistake in preprocessing pipelines is data leakage β computing scaling parameters (mean, std, min, max) on the entire dataset before splitting into train/test.
This causes the model to βseeβ 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.
Thanks for your feedback!
