MLS-C01 Modeling Practice Question
A data scientist is working on a regression problem with a dataset that contains outliers. The data scientist is choosing between mean squared error (MSE) and mean absolute error (MAE) as the loss function. Which loss function is more robust to outliers?
⚠ Common exam trap
AWS often tests the misconception that a loss function that penalizes errors more heavily is better for robustness, when in fact the opposite is true for 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
✓
MAE, because it treats all errors equally.
MAE is more robust to outliers because it uses the absolute difference between predicted and actual values, which does not disproportionately penalize large errors. In contrast, MSE squares the errors, causing outliers to have a much larger influence on the loss and model updates. This makes MAE less sensitive to extreme values in regression tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Both are equally robust to outliers.
Why it's wrong here
MSE is more sensitive to outliers than MAE.
- ✗
MSE, because it penalizes large errors more heavily.
Why it's wrong here
Penalizing large errors more makes MSE more sensitive to outliers.
- ✓
MAE, because it treats all errors equally.
Why this is correct
MAE is linear in errors, reducing the impact of outliers.
- ✗
Neither is robust; use Huber loss instead.
Why it's wrong here
While Huber loss is robust, MAE is still more robust than MSE.
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