MLS-C01 Modeling Practice Question
A data scientist is using SageMaker to train a linear regression model. The target variable has a long-tail distribution. Which data transformation is LEAST likely to improve model performance?
⚠ Common exam trap
Many exam-takers confuse feature scaling (normalization) with target transformation, assuming that normalizing features will also fix target skewness, but the question specifically asks about the transformation least likely to improve performance for a long-tail target distribution.
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
✓
Normalize all feature values to [0,1]
Normalizing feature values to [0,1] is a scaling technique that does not address the long-tail distribution of the target variable. Long-tail distributions typically require transformations that compress the tail (e.g., log or Box-Cox) to make the relationship more linear and reduce the influence of extreme values. Feature normalization helps with gradient descent convergence but does not fix skewness in the target, so it is least likely to improve model performance for this specific issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add interaction terms between features
Why it's wrong here
Interaction terms can capture non-linear relationships.
- ✗
Apply log transformation to the target variable
Why it's wrong here
Log transformation can reduce skewness and improve model fit.
- ✓
Normalize all feature values to [0,1]
Why this is correct
Normalization does not affect linear regression's coefficients; it's not needed.
- ✗
Remove outliers from the target variable
Why it's wrong here
Removing outliers can improve the model's performance.
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