MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is analyzing a dataset that contains a categorical feature 'country' with 200 unique values. The target variable is binary. The engineer wants to use this feature in a linear model. Which encoding method should be applied during EDA to prepare the data for modeling, considering the high cardinality?
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 with cross-validation
Target encoding with cross-validation (Option A) is the correct choice for this scenario because it replaces each category in the high-cardinality feature 'country' with the mean of the target variable, effectively capturing the relationship with the target while avoiding the curse of dimensionality. Cross-validation is essential to prevent overfitting by computing the target means on out-of-fold data. One-hot encoding (Option D) would create 199 dummy variables, leading to high dimensionality and potential overfitting, making it unsuitable for linear models with limited data. Label encoding (Option B) imposes an arbitrary ordinal relationship that the linear model would misinterpret. Frequency encoding (Option C) may not capture the relationship with the target and could lose predictive power.
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 with cross-validation
Why this is correct
Target encoding captures the relationship with the target, and cross-validation prevents data leakage.
- ✗
Label encoding
Why it's wrong here
Label encoding assumes an ordinal relationship that does not exist.
- ✗
Frequency encoding
Why it's wrong here
Frequency encoding uses count but may lose predictive information.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding creates too many features, which can cause the curse of dimensionality.
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