Explain leakage, missing data, and common losses

Q: Explain leakage, missing data, and common losses

This question evaluates a candidate's understanding of data leakage, strategies for handling missing data, and the differences between loss functions used in linear and logistic regression (including trade-offs between MSE and MAE), probing competency in data preprocessing, robustness, and model evaluation within Machine Learning.

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Question

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Answer the following traditional ML questions:

Data leakage
- What is data leakage?
- Give 2–3 common examples.
- How do you prevent or fix it in practice?
Missing data
- What are common strategies to handle missing values?
- When might you drop rows/columns vs impute?
- How can missingness itself be informative?
Linear vs logistic regression losses
- What loss is commonly used for linear regression? For logistic regression?
- Compare MSE vs MAE : how do they differ, and when might you prefer one?

Explain leakage, missing data, and common losses

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