Selecting the Right Technique
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.
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.
Merci pour vos commentaires !
Demandez à l'IA
Demandez à l'IA
Posez n'importe quelle question ou essayez l'une des questions suggérées pour commencer notre discussion
Can you explain more about when to choose each scaling technique?
What are the consequences of using the wrong scaling method?
Can you give examples of data leakage in real-world scenarios?
Awesome!
Completion rate improved to 5.26Selecting the Right Technique
Glissez pour afficher le menu
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.
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.
Merci pour vos commentaires !
