MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a linear regression model. The dataset has outliers. Which TWO techniques can help reduce the impact of outliers? (Choose TWO.)
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
✓
Trim the dataset to remove extreme values
Options A and D are correct. Huber loss is robust to outliers, and trimming the dataset removes extreme values. Option B (more features) is not relevant for handling outliers. Option C (L1 regularization) reduces overfitting but not outlier impact. Option E (standardization) does not handle outliers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Trim the dataset to remove extreme values
Why this is correct
Removing outliers reduces their influence on the model.
- ✗
Add more features
Why it's wrong here
More features do not address outliers.
- ✗
Apply L1 regularization
Why it's wrong here
Regularization helps with overfitting, not outliers.
- ✓
Use Huber loss instead of squared error
Why this is correct
Huber loss is less sensitive to outliers than squared error.
- ✗
Standardize the features
Why it's wrong here
Standardization does not remove outlier influence.
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