AI0-001 Machine Learning and Deep Learning Practice Question
An organization has a dataset with categorical features having high cardinality (e.g., ZIP codes). They plan to use a tree-based model. Which encoding method is most appropriate?
⚠ Common exam trap
CompTIA often tests the misconception that one-hot encoding is always the safest choice for categorical data, but the trap here is that high cardinality makes one-hot encoding impractical, and candidates overlook target encoding as a cardinality-efficient alternative.
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
✓
Target encoding (mean encoding)
Target encoding (mean encoding) replaces each category with the mean of the target variable for that category, which works well with tree-based models on high-cardinality features because it captures the predictive signal without exploding the feature space. This method avoids the dimensionality explosion of one-hot encoding and the arbitrary ordering of label encoding, making it the most appropriate choice for high-cardinality categorical features in tree-based models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Label encoding
Why it's wrong here
Label encoding assigns arbitrary integers, implying order that misleads tree splits.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding creates high-dimensional sparse data, which can degrade tree performance.
- ✓
Target encoding (mean encoding)
Why this is correct
Target encoding maps categories to the mean target, preserving predictive information compactly.
- ✗
Frequency encoding
Why it's wrong here
Frequency encoding uses category counts; target encoding is more directly predictive.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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