MLS-C01 Modeling Practice Question
A data scientist is building a regression model to predict house prices. The dataset contains 10 features, including 'number_of_bedrooms' and 'square_footage'. The scientist observes that the model has high variance. Which TWO actions are most appropriate to reduce overfitting? (Choose TWO.)
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that adding more data or features always improves model performance, but the trap here is that reducing training data or adding polynomial features increases variance, while regularization and simpler models are the correct countermeasures for overfitting.
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
✓
Reduce model complexity by using a simpler model
A is correct because reducing model complexity, such as using a simpler model (e.g., linear regression instead of a high-degree polynomial), directly decreases variance by limiting the model's capacity to fit noise in the training data. This aligns with the bias-variance tradeoff, where simpler models have higher bias but lower variance, making them less prone to overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Reduce model complexity by using a simpler model
Why this is correct
Simpler models have lower variance.
- ✓
Add L2 regularization to the model
Why this is correct
L2 regularization penalizes large weights, reducing overfitting.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs can lead to overfitting if not early-stopped.
- ✗
Decrease the amount of training data
Why it's wrong here
Less data often increases variance.
- ✗
Add more polynomial features
Why it's wrong here
Adding features increases model complexity, which can increase variance.
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