MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with 100 features. After generating pair plots, the scientist notices that many features have skewed distributions. Which transformation should the scientist apply to make the distributions more Gaussian-like for modeling?
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
(Yeo-Johnson transformation) is correct because it can handle both positive and negative values, making it suitable for datasets with skewed distributions that may include negative numbers. Option A (Log transformation) is wrong because it only works for strictly positive values. Option C (Standard scaling) does not change the shape of the distribution; it only centers and scales the data, so it does not fix skewness. Option D (Box-Cox transformation) also requires positive values, limiting its applicability.
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 it's wrong here
Only for positive values.
- ✓
Yeo-Johnson transformation
Why this is correct
Works for any real values.
- ✗
Standard scaling (z-score normalization)
Why it's wrong here
Does not correct skewness.
- ✗
Box-Cox transformation
Why it's wrong here
Requires positive values.
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