MLS-C01 Modeling Practice Question
A data scientist is using SageMaker to train an XGBoost model for regression. The training data contains categorical features with high cardinality (e.g., zip code with over 10,000 unique values). Which feature engineering approach is MOST appropriate to avoid overfitting while preserving predictive power?
⚠ Common exam trap
A common mix-up: candidates default to one-hot encoding for categorical features, not realizing that high cardinality makes it computationally infeasible and prone to overfitting, while target encoding with smoothing offers a compact and powerful alternative.
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 smoothing
Target encoding with smoothing is the most appropriate approach because it replaces each high-cardinality category with the mean of the target variable for that category, regularized by a smoothing factor that pulls estimates toward the global mean. This preserves predictive power by capturing the relationship between the category and the target while preventing overfitting on rare categories that have few samples. In SageMaker XGBoost, this avoids the curse of dimensionality from one-hot encoding and the arbitrary ordering from label encoding.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use target encoding with smoothing
Why this is correct
Target encoding captures category-target relationship with regularization to avoid overfitting.
- ✗
One-hot encode the categorical features
Why it's wrong here
One-hot encoding creates excessive features, leading to sparsity and overfitting.
- ✗
Apply frequency encoding based on category occurrence
Why it's wrong here
Frequency encoding may not capture target-specific patterns.
- ✗
Label encode the categorical features
Why it's wrong here
Label encoding assumes ordinality, which may not exist.
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