MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with many missing values. The scientist wants to decide on an imputation strategy. Which THREE considerations are important for choosing the imputation method?
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
✓
The mechanism of missingness (MCAR, MAR, MNAR).
The three correct considerations are: missing data mechanism (MCAR/MAR/MNAR) which determines whether imputation can be unbiased; percentage of missing values in each feature, which affects the reliability of imputation and whether deletion is preferable; and feature distribution (e.g., skewed, normal), which guides the choice between mean, median, or model-based imputation. Option B (class imbalance) is a consideration for classification models, not imputation. Option E (feature importance) is not a standard criterion for choosing imputation methods.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The mechanism of missingness (MCAR, MAR, MNAR).
Why this is correct
Determines whether imputation is valid.
- ✗
The class imbalance of the target variable.
Why it's wrong here
Class imbalance is a separate issue.
- ✓
The percentage of missing values in each feature.
Why this is correct
High missingness may require different strategies.
- ✓
The distribution of the feature (e.g., skewed, normal).
Why this is correct
Mean imputation is inappropriate for skewed data.
- ✗
The feature importance according to a random forest model.
Why it's wrong here
Feature importance does not guide imputation method.
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