What is the primary purpose of performing feature scaling in a machine learning pipeline?

Question

Data Science — Easy

What is the primary purpose of performing feature scaling in a machine learning pipeline?

Accepted Answer

Feature scaling, such as normalization or standardization, ensures that features with larger numerical ranges do not disproportionately influence algorithms that rely on distance metrics, such as K-Nearest Neighbors or Support Vector Machines. Option A refers to dimensionality reduction techniques like PCA. Option C is incorrect because scaling does not address missing values. Option D refers to encoding techniques like One-Hot Encoding.