MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with skewed numerical features. Which THREE transformations can help make the features more normally distributed?
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
✓
Yeo-Johnson transformation
Correct options: C, D, E. Yeo-Johnson transformation (C), Box-Cox transformation (D), and log transformation (E) are all effective for making skewed numerical features more normally distributed. Option A, min-max scaling, only rescales the feature to a fixed range and does not change the distribution shape. Option B, standardization (Z-score), centers and scales the data but does not alter skewness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Min-max scaling
Why it's wrong here
Incorrect: Min-max scaling does not alter distribution shape.
- ✗
Standardization (Z-score)
Why it's wrong here
Incorrect: Standardization centers and scales but does not change shape.
- ✓
Yeo-Johnson transformation
Why this is correct
Correct: Yeo-Johnson works for both positive and negative values.
- ✓
Box-Cox transformation
Why this is correct
Correct: Box-Cox can handle various skewness.
- ✓
Log transformation
Why this is correct
Correct: Log transform reduces right skew.
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