AI Associate Data for AI Practice Question
During data transformation, a data scientist applies one-hot encoding to a categorical feature with 50 unique values. The resulting dataset has 50 new columns. What is a potential drawback of this transformation?
⚠ Common exam trap
Salesforce often tests the misconception that one-hot encoding always improves model performance by preserving all information, when in fact high cardinality introduces sparsity and overfitting risks that can degrade model accuracy.
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
✓
High cardinality leading to sparse data and overfitting
One-hot encoding a categorical feature with 50 unique values creates 50 binary columns, each representing one category. This high cardinality leads to a very sparse matrix (most entries are 0), which can cause the model to overfit by learning noise from rare categories, especially when the dataset is not large enough to support such dimensionality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduction in training time
Why it's wrong here
More features increase training time.
- ✗
Increased interpretability of the model
Why it's wrong here
One-hot can make models less interpretable with many columns.
- ✓
High cardinality leading to sparse data and overfitting
Why this is correct
High cardinality creates many sparse columns, risking overfitting.
- ✗
Loss of ordinal information in categories
Why it's wrong here
Ordinal info is lost, but that's not the main drawback here.
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