MLS-C01 Winsorization Practice Question
During EDA, a data scientist finds that a numeric feature has many outliers. The feature will be used in a linear regression model. Which approach should the scientist take to handle the outliers?
⚠ Common exam trap
Candidates often confuse capping (winsorization) with standardization or transformation. Standardization does not mitigate outliers; it only rescales the data. The key is to limit extreme values using percentile-based capping.
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
✓
Cap the feature values at the 1st and 99th percentiles.
Capping (winsorizing) the feature values at the 1st and 99th percentiles limits the influence of extreme outliers while retaining all data points. This is particularly important for linear regression, which is sensitive to outliers. Option A is wrong because removing all rows with outliers can lead to significant data loss and bias. Option B is wrong because a logarithmic transformation reduces skew but does not eliminate the impact of outliers; it only compresses their range. Option C is wrong because Z-score normalization standardizes the data but does not reduce the influence of outliers; extreme values remain extreme relative to the distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all rows with outlier values.
Why it's wrong here
Incorrect. Removing all rows with outliers can discard valuable information and introduce bias, especially if outliers are natural variations.
- ✗
Apply a logarithmic transformation to the feature.
Why it's wrong here
Incorrect. Log transformation reduces skewness but does not eliminate the influence of extreme outliers; they remain as high or low values on the log scale.
- ✗
Standardize the feature using Z-score normalization.
Why it's wrong here
Incorrect. Z-score normalization rescales data but does not reduce the magnitude of outliers relative to the rest of the data, so linear regression is still affected.
- ✓
Cap the feature values at the 1st and 99th percentiles.
Why this is correct
Correct. Capping at percentiles limits extreme values, reducing their impact while preserving data size.
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