MLS-C01 Modeling Practice Question
A data scientist is performing feature engineering for a machine learning model. The dataset contains categorical features with high cardinality. Which THREE techniques are appropriate for encoding high-cardinality categorical features?
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 (A) replaces each category with the mean of the target variable, which captures predictive signal and is efficient for high cardinality. Count encoding (D) uses the frequency of each category, providing a simple numeric representation. One-hot encoding with pruning of rare categories (E) reduces dimensionality by creating dummy variables only for frequent categories, avoiding the curse of dimensionality. Binary encoding (B) is not among the most recommended techniques; it converts categories to binary numbers but can be less intuitive and may not handle rare categories well. Label encoding (C) assigns integers arbitrarily, implying ordinal relationships that may mislead the model, making it unsuitable for nominal high-cardinality features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Target encoding
Why this is correct
Replaces category with target mean.
- ✗
Binary encoding
Why it's wrong here
Valid but not among the three selected.
- ✗
Label encoding
Why it's wrong here
Implies ordinality, not suitable.
- ✓
Count encoding
Why this is correct
Uses frequency count.
- ✓
One-hot encoding with pruning of rare categories
Why this is correct
Pruning reduces dimensionality.
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