MLS-C01 Exploratory Data Analysis Practice Question
Which TWO actions are appropriate when dealing with outliers in a dataset during exploratory data analysis? (Select TWO.)
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between data transformation techniques (like log transformation) and data removal or replacement strategies, trapping candidates who think that simply changing a summary statistic (mean to median) or deleting outliers without investigation is a proper handling method.
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 log transformation to reduce the impact of extreme values.
Applying a log transformation compresses the range of the data, reducing the influence of extreme values without removing them. This is a common technique in exploratory data analysis for right-skewed distributions, as it can make the data more normally distributed and improve the performance of models that assume normality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace the mean with the median for numerical features.
Why it's wrong here
This does not handle outliers directly; consider transformations or robust methods.
- ✓
Apply log transformation to reduce the impact of extreme values.
Why this is correct
Log transformation can compress skewed distributions and reduce outlier influence.
- ✗
Remove all outliers without further investigation.
Why it's wrong here
Outliers may be valid data points; investigate before removal.
- ✓
Use visualization techniques like box plots to identify outliers.
Why this is correct
Visualizations help understand the distribution and identify outliers.
- ✗
Assume outliers are errors and delete them.
Why it's wrong here
Outliers may be genuine; deleting without analysis can lead to loss of information.
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