MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing feature engineering on a dataset with high cardinality categorical features (e.g., ZIP codes with thousands of unique values). Which technique is most effective for reducing dimensionality while preserving predictive power?
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 (also known as mean encoding) replaces each category with the mean of the target variable for that category. This preserves the predictive signal by directly encoding the relationship between the category and the target, while reducing the dimensionality to a single continuous feature. Hash encoding can cause collisions and loss of information. One-hot encoding creates too many dummy variables for high cardinality features. Label encoding imposes an arbitrary ordinal relationship that may not exist and can mislead 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.
- ✗
Hash encoding
Why it's wrong here
Incorrect: Hash encoding can lead to collisions and is not interpretable.
- ✗
One-hot encoding
Why it's wrong here
Incorrect: One-hot encoding increases dimensionality, not reduces it.
- ✓
Target encoding
Why this is correct
Correct: Target encoding reduces cardinality by using target statistics, preserving predictive power.
- ✗
Label encoding
Why it's wrong here
Incorrect: Label encoding imposes arbitrary ordinal relationships.
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