MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working with a dataset that contains a feature with many outliers. Which transformation should the scientist apply to reduce the impact of outliers?
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 compresses the range of values and reduces the impact of outliers. Standardization (z-score) does not reduce outlier impact. Min-max scaling is sensitive to outliers. Square root transformation is less effective than log for large outliers. Binning loses information.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Min-max scaling
Why it's wrong here
Min-max scaling is highly influenced by extreme values.
- ✓
Log transformation
Why this is correct
Log transformation reduces skewness and dampens outlier effects.
- ✗
Standardization (z-score)
Why it's wrong here
Standardization uses mean and standard deviation, which are affected by outliers.
- ✗
Binning
Why it's wrong here
Binning reduces information and is not a transformation that reduces outlier impact directly.
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