MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset and notices that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution more symmetric?
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 (natural log)
Log transformation is commonly used to reduce right skewness by compressing the range of large values. Option B is wrong because Min-Max scaling only rescales the data to a fixed range and does not alter the distribution shape, so it cannot reduce skewness. Option C is wrong because one-hot encoding is designed for categorical features and does not apply to continuous features. Option D is wrong because a square transformation (power >1) amplifies larger values more than smaller ones, which would increase right skewness rather than reduce it.
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 (natural log)
Why this is correct
Log transformation compresses high values, reducing right skew.
- ✗
Min-Max scaling
Why it's wrong here
Scaling does not change distribution shape.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables.
- ✗
Square transformation
Why it's wrong here
Square transformation amplifies skewness.
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