Handle missing values for LGD modeling evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
How would you handle missing values in a dataset used for LGD modeling? Compare multiple imputation, model‑based imputation, business‑rule fills, indicator variables, and conditions where leaving missingness explicit is preferable.
Quick Answer: Handle missing values for LGD modeling evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
You are building a Loss Given Default (LGD) model using account- and borrower-level features captured around the time of default. The dataset contains both continuous and categorical variables with non-trivial missingness due to reporting gaps, system migrations, and process differences across products/regions.
Task
Describe how you would handle missing values in this LGD modeling context. Specifically, compare the following approaches:
Multiple imputation (e.g., MICE)
Model-based imputation (e.g., kNN, random forest, regression)