Question 1,494 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is building a binary classification model to predict whether a customer will subscribe to a service. The dataset contains 20 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). The scientist uses a logistic regression model and obtains a training AUC of 0.85 and a test AUC of 0.60. The scientist suspects overfitting due to high cardinality features. Which approach should the scientist use to address this issue?
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
✓
Apply target encoding with smoothing to the zip code feature
(target encoding with smoothing) reduces cardinality while preserving predictive power. Option A (label encoding) may introduce ordinality issues. Option B (remove zip code) may lose important information. Option D (one-hot encoding) increases dimensionality drastically.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply label encoding to the zip code feature
Why it's wrong here
Label encoding implies ordinal relationship, which is inappropriate.
- ✗
Remove the zip code feature entirely
Why it's wrong here
Removing may discard useful geographic information.
- ✓
Apply target encoding with smoothing to the zip code feature
Why this is correct
Target encoding reduces cardinality and can improve generalization.
- ✗
Apply one-hot encoding to the zip code feature
Why it's wrong here
One-hot encoding creates thousands of sparse features, worsening overfitting.
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Last reviewed: Jun 20, 2026
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