MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is evaluating feature engineering options for a dataset containing a categorical variable 'education_level' with values: High School, Bachelor, Master, PhD. The target variable is continuous. Which THREE encoding methods are appropriate for this ordinal categorical variable? (Choose 3)
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
✓
One-hot encoding
Options A, B, and D are correct: One-hot encoding (A) can be used for ordinal variables, though it ignores order, it is still valid. Target encoding (B) captures the relationship with the target and respects ordinality. Label encoding (D) preserves the ordinal nature. Option C (hash encoding) is incorrect because it is typically used for high-cardinality nominal variables, not ordinal, and may lose interpretability. Option E (binary encoding) is also incorrect because it is designed for nominal categories and does not maintain order.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
One-hot encoding
Why this is correct
One-hot encoding is a safe option that does not assume any order, though it increases dimensionality.
- ✓
Target encoding (mean of target per category)
Why this is correct
Target encoding can capture the effect of each category on the target.
- ✗
Hash encoding (using feature hashing)
Why it's wrong here
Hash encoding is for high cardinality nominal variables and loses ordinal information.
- ✓
Label encoding (e.g., High School=0, Bachelor=1, Master=2, PhD=3)
Why this is correct
Label encoding preserves the ordinal relationship.
- ✗
Binary encoding (convert to binary representation)
Why it's wrong here
Binary encoding is for nominal variables and does not preserve ordinality.
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