Standardization › When to Use Standardization

Standardization is especially important for certain types of machine learning algorithms, while for others it is less critical.

Algorithms that require standardization include:

• LASSO and Ridge regression: These methods use regularization penalties that assume all features are on the same scale. Without standardization, features with larger numeric ranges can dominate the penalty and skew the model.
• k-means clustering: Since this algorithm relies on distance calculations, features must be on comparable scales to ensure that no single variable dominates the clustering process.
• Support Vector Machines: The kernel functions used in SVMs are distance-based.
• Neural networks: Gradient-based optimization in neural networks converges more efficiently when inputs are normalized.
• Principal Component Analysis (PCA): As PCA is based on variance, standardizing features ensures that each variable contributes appropriately to the dimensionality reduction.

When standardization is less critical:

• Decision trees: These algorithms are scale-invariant, meaning their splitting criteria are unaffected by the magnitude of input variables.
• Random Forest: As an ensemble of decision trees, Random Forests inherit this scale invariance.
• Logistic regression: While standardization is not strictly required, it becomes important when regularization is used, as the penalty terms are sensitive to feature scale.