MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is analyzing feature distributions in a dataset and notices that one feature has a long tail. Which transformation is most appropriate to reduce skewness and make the distribution more 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
✓
Apply a log transformation
Log transformation is the most appropriate technique to reduce right skewness (long tail) and make the distribution closer to normal. One-hot encoding is used for categorical variables, not for transforming skewed numerical features. Min-max normalization scales features to a range but does not change the shape of the distribution. Standardization (Z-score) centers the data and scales by standard deviation, but also does not reduce skewness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply one-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables.
- ✓
Apply a log transformation
Why this is correct
Log transformation compresses the long tail and reduces skewness.
- ✗
Apply min-max normalization
Why it's wrong here
Min-max scaling does not change distribution shape.
- ✗
Apply standardization (Z-score)
Why it's wrong here
Standardization centers and scales but does not reduce skewness.
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