MLS-C01 Modeling Practice Question
A machine learning team is building a model to predict customer churn. They have a dataset with 10,000 samples and 50 features, including categorical variables with high cardinality (e.g., ZIP code). Which feature engineering technique is most appropriate to reduce dimensionality while preserving predictive information?
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 high-cardinality categories with the mean target value, reducing dimensionality while capturing predictive signal. Option A (PCA) is wrong because PCA is applied to numerical features, not categorical. Option B (One-hot encoding) is wrong because one-hot encoding creates many sparse features, increasing dimensionality. Option D is wrong because label encoding imposes ordinality that may not exist.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is applied to numerical features and does not handle categorical variables directly; it would not reduce dimensionality from high-cardinality categories.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding creates many sparse features for high-cardinality categories, increasing dimensionality rather than reducing it.
- ✓
Target encoding
Why this is correct
Target encoding reduces dimensionality by replacing categories with target mean, preserving predictive information.
- ✗
Label encoding
Why it's wrong here
Label encoding assigns arbitrary integers, which can introduce false ordinal relationships.
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