DA0-002 Data Analysis Practice Question
A dataset contains features with vastly different scales (e.g., age 0-100 and income 0-1,000,000). Which data transformation should be applied before using a K-nearest neighbors algorithm?
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
✓
Min-max normalization
Distance-based algorithms like KNN require features on similar scales; min-max normalization is appropriate.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
No transformation is needed
Why it's wrong here
Different scales would bias the distance metric.
- ✓
Min-max normalization
Why this is correct
Min-max scales features to a fixed range (0-1), suitable for distance-based methods.
- ✗
Log transformation
Why it's wrong here
Log transform reduces skew but does not normalize to a common scale.
- ✗
Z-score standardization
Why it's wrong here
Standardization centers data, but normalization is often preferred for distance-based algorithms.
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