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Identify Risks and Improve Imputation Class Implementations

Capital OneData Scientist · Onsite
MediumData ScientistOnsiteMachine Learning

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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 Identify Risks and Improve Imputation Class Implementations states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

Tech round code-review: three imputation classes implementing mean, median, and mode substitution for missing values

Question

Identify any problems or risks you notice in these imputation class implementations.

Suggest concrete improvements or refactors to make them more robust and reusable.

Hints

Consider inheritance, dtype handling, sparse data, incremental fit, edge cases, and compliance with sklearn interface.