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MLA-C01 Practice Question: A data scientist is working with a dataset that…
A data scientist is working with a dataset that contains missing values in several numerical columns. The missing data is not completely at random (MNAR). The scientist wants to minimize bias in the imputed values. Which imputation strategy is most appropriate?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a model-based imputation like iterative imputer or KNN imputer
Model-based imputation methods, such as using a regressor to predict missing values based on other features, can capture complex relationships and reduce bias compared to simple mean/median imputation, especially when data is not MCAR.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delete all rows with missing values
Why it's wrong here
Listwise deletion can discard a large portion of data and may introduce bias.
- ✓
Use a model-based imputation like iterative imputer or KNN imputer
Why this is correct
Model-based imputation uses correlations between features to estimate missing values, reducing bias in MNAR settings.
- ✗
Replace missing values with the median of each column
Why it's wrong here
Median imputation also assumes MCAR and does not account for dependencies.
- ✗
Replace missing values with the mean of each column
Why it's wrong here
Mean imputation assumes MCAR and can introduce bias in MNAR scenarios.
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