MLS-C01 Exploratory Data Analysis Practice Question
During EDA, a data scientist notices that a feature has a high proportion of missing values (e.g., 70%). The feature is continuous and expected to be important based on domain knowledge. What is the best approach to handle this?
⚠ Common exam trap
A common mix-up: candidates choose to drop the feature or rows without considering that missingness can be a meaningful signal, and that a binary indicator combined with robust imputation is a standard technique for high-missingness continuous features.
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
✓
Create a binary indicator for missingness and impute the continuous values with the median.
It preserves the predictive signal from the feature while accounting for the pattern of missingness. Creating a binary indicator allows the model to learn whether missingness itself is informative, and median imputation is robust to outliers for a continuous feature. This approach avoids the bias of dropping the feature entirely and is more principled than arbitrary out-of-range imputation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the feature entirely to avoid bias.
Why it's wrong here
Removing a potentially important feature is not optimal.
- ✓
Create a binary indicator for missingness and impute the continuous values with the median.
Why this is correct
This captures both the pattern of missingness and the distribution.
- ✗
Impute missing values with -1 since it is out of range.
Why it's wrong here
Arbitrary constant can distort the distribution.
- ✗
Drop all rows with missing values in that feature.
Why it's wrong here
Dropping 70% of rows is wasteful.
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