MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with 100,000 observations and 50 features. The scientist uses a Jupyter notebook on Amazon SageMaker. During EDA, the scientist runs a command to check for missing values and notices that 20% of the data in one feature is missing. The missing values are not random; they are correlated with another feature. Which imputation method 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
✓
Multiple imputation by chained equations (MICE)
MICE uses multiple imputation based on other features, accounting for the correlation between the missing feature and another feature. Option A is wrong because median imputation ignores the correlation and simply fills with the median, which does not leverage relationships between features. Option B is wrong because listwise deletion removes rows with missing data, which reduces sample size and can introduce bias if missingness is not completely random. Option C is wrong because mean imputation, like median imputation, ignores correlations and can distort relationships.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Median imputation
Why it's wrong here
Median imputation ignores the correlation between features and may not capture the underlying pattern of missing data.
- ✗
Listwise deletion (remove rows with missing values)
Why it's wrong here
Listwise deletion removes entire rows with missing values, leading to data loss and potential bias if missingness is not completely random.
- ✗
Mean imputation
Why it's wrong here
Mean imputation ignores the correlation between features and assumes missing values are random, which is not the case here.
- ✓
Multiple imputation by chained equations (MICE)
Why this is correct
Models missing values using other features.
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