MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is analyzing a dataset and observes that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution approximately normal?
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
A log transformation (C) is most appropriate for heavily right-skewed continuous data because it compresses the long right tail and can make the distribution approximately normal. Square root (A) is less effective for severe skewness. Exponential (B) would amplify the skewness. Box-Cox with lambda = 0 (D) is equivalent to log, but since log is explicitly given and commonly known, option C is the direct and correct choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Square root transformation
Why it's wrong here
Square root is for moderate skew, not heavy skew.
- ✗
Exponential transformation
Why it's wrong here
Exponential would increase skew.
- ✓
Log transformation
Why this is correct
Log transformation is standard for right-skewed data.
- ✗
Box-Cox transformation with lambda = 0
Why it's wrong here
Box-Cox transformation with lambda = 0 is equivalent to a natural logarithm transformation. While a log transformation is effective for reducing right-skewness, fixing lambda at 0 may not be the optimal power transformation to achieve approximate normality for all heavily skewed datasets. The Box-Cox method's strength lies in its ability to determine the most suitable lambda value from a range, which could be different from 0, to best normalise the data. It is tempting because log transformation is a common initial approach for right-skewed data.
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