MLS-C01 Exploratory Data Analysis Practice Question
During EDA of a dataset for a regression problem, a data scientist notices that the target variable has a right-skewed distribution. Which THREE transformations are appropriate to address this skewness? (Choose THREE.)
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
✓
Log transformation
Options A, C, and D are correct. Log transformation, Box-Cox transformation, and Yeo-Johnson transformation are effective methods for reducing right skewness in the target variable. Option B (StandardScaler) standardizes features to have zero mean and unit variance, but does not reduce skewness. Option E (Min-Max scaling) scales features to a fixed range, but does not affect the shape of the distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Log transformation
Why this is correct
Log transformation compresses large values, reducing right skew.
- ✗
StandardScaler (z-score normalization)
Why it's wrong here
StandardScaler applies a linear shift and scale to achieve zero mean and unit variance, but it does not alter the shape of a distribution; a right-skewed distribution remains right-skewed after z-score normalisation. It is tempting because standardisation is a common preprocessing step for many regression algorithms, and it would be correct when the goal is to centre features for models like linear regression or SVM, not to correct skewness.
- ✓
Box-Cox transformation
Why this is correct
Box-Cox is designed to make data more normally distributed.
- ✓
Yeo-Johnson transformation
Why this is correct
Yeo-Johnson is similar to Box-Cox but works with zero and negative values.
- ✗
Min-Max scaling
Why it's wrong here
Min-Max scaling does not alter skewness.
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