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Scale and Normalize: When to Use Each Method?

Boston Consulting GroupData Scientist · Online Assessment
EasyData ScientistOnline AssessmentMachine Learning

Last updated

This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Scale and Normalize: When to Use Each Method? states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

BCG CodeSignal notebook – feature scaling step before modeling

Question

Given a DataFrame df with numeric columns age and income, demonstrate how to Standard-scale age and Min-Max normalize income. Explain when you would prefer each scaler.

Hints

Use sklearn.preprocessing; relate to Gaussian vs bounded distributions.