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MLA-C01 Practice Question: Which technique is commonly used to handle…
Which technique is commonly used to handle missing values in a categorical feature?
⚠ Common exam trap
The MLA-C01 exam often tests the distinction between data preprocessing techniques (imputation) and feature engineering techniques (encoding, scaling), leading candidates to mistakenly select one-hot encoding as a missing-value handler because it is commonly associated with categorical data.
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
✓
Mode imputation
Mode imputation is the standard technique for handling missing values in categorical features because it replaces missing entries with the most frequent category, preserving the feature's distribution without introducing artificial values. Unlike numerical imputation methods, mode imputation respects the non-numeric nature of categorical data and maintains the integrity of the original categories.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding transforms categories into binary vectors but does not handle missing values.
- ✗
Mean imputation
Why it's wrong here
Mean imputation is appropriate for numerical features, not categorical.
- ✓
Mode imputation
Why this is correct
Mode imputation replaces missing categorical values with the most frequent category, a common practice.
- ✗
Standard scaling
Why it's wrong here
Standard scaling normalizes numerical features, not for missing values.
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