MLS-C01 Modeling Practice Question
A team is building a model to predict customer churn. They have 50 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). Which feature engineering technique is most appropriate?
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
Target encoding replaces each category with the mean of the target variable, which handles high cardinality well. Option A (binning) reduces cardinality but loses information. Option B is correct because target encoding is specifically designed for high-cardinality categorical features. Option C (label encoding) implies ordinality and can introduce misleading relationships. Option D (one-hot encoding) would create 10,000 binary columns, causing high dimensionality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Binning zip codes into regions
Why it's wrong here
Binning loses granular information and may not capture local patterns.
- ✓
Target encoding
Why this is correct
Target encoding condenses high cardinality into one numeric feature.
- ✗
Label encoding
Why it's wrong here
Label encoding imposes arbitrary ordinal relationships.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding creates too many features, leading to curse of dimensionality.
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