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MLA-C01 Practice Question: A data scientist is building a regression model…
A data scientist is building a regression model to predict house prices. The dataset contains a feature 'neighborhood' with 500 distinct values, and most neighborhoods have fewer than 10 samples. Which approach is MOST appropriate for handling this high-cardinality categorical feature?
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
✓
Use target encoding with proper cross-validation to avoid data leakage
Target encoding replaces each category with the mean target value, which is effective for high-cardinality features while maintaining predictive power. One-hot encoding would create too many sparse columns, and label encoding would impose an arbitrary ordinal relationship.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Drop the neighborhood feature entirely
Why it's wrong here
Dropping the feature discards potentially valuable information unnecessarily.
- ✗
Apply frequency encoding, replacing each neighborhood with its count in the training set
Why it's wrong here
Frequency encoding may lose predictive signal and does not directly relate to the target variable.
- ✗
One-hot encode the feature and use L1 regularization
Why it's wrong here
One-hot encoding 500 categories would create 500 dummy variables, causing sparsity and memory issues; L1 regularization may help but is not the most appropriate primary approach.
- ✓
Use target encoding with proper cross-validation to avoid data leakage
Why this is correct
Target encoding effectively captures the relationship between categories and target, and cross-validation prevents overfitting.
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