MLS-C01 Modeling Practice Question
A data scientist is working with a dataset containing categorical features with high cardinality. The scientist wants to use a tree-based model. Which encoding method should be used?
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
✓
Label encoding
For tree-based models, label encoding (option C) is typically recommended for high-cardinality categorical features because tree models can handle integer encoding without assuming any order—they split on values, not on ordinal relationships. Ordinal encoding (option A) implies an artificial order that may not exist, potentially misleading the model. One-hot encoding (option D) creates too many dimensions. Target encoding (option B) can cause overfitting, especially with high cardinality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Ordinal encoding
Why it's wrong here
Ordinal encoding assigns integers with an implied order, which can mislead tree-based models if no natural order exists. It is not the standard choice for high-cardinality features.
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Target encoding
Why it's wrong here
Target encoding replaces categories with the mean of the target, which can cause overfitting, especially with high cardinality. Regularization techniques are needed to mitigate this.
- ✓
Label encoding
Why this is correct
Label encoding assigns arbitrary integers to categories. Tree-based models can use these integers effectively because they split on feature values without assuming order. This avoids expanding the feature space.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding creates binary columns for each category, leading to a large number of features for high cardinality, which can reduce performance and increase memory usage.
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