MLS-C01 Exploratory Data Analysis Practice Question
During exploratory data analysis, a data scientist notices that the distribution of a continuous feature is heavily right-skewed. Which transformation should be applied to make the distribution more symmetric for linear regression?
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
Log transformation is commonly used to reduce right skewness and make the distribution more symmetric. Standardization (z-score) does not change the shape of the distribution; it only centers and scales. One-hot encoding is for categorical features, not continuous. Min-max scaling also does not affect skewness; it rescales the range but preserves shape.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Standardization (z-score)
Why it's wrong here
Standardization does not fix skewness.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical features.
- ✗
Min-max scaling
Why it's wrong here
Scaling does not change distribution shape.
- ✓
Log transformation
Why this is correct
Log transformation reduces right skewness.
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