MLS-C01 Modeling Practice Question
A team is building a regression model to predict house prices. They observe that the model performs well on training data but poorly on validation data. Which THREE actions can help reduce overfitting? (Choose THREE.)
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 selecting fewer features
(reduce model complexity by selecting fewer features) reduces overfitting by limiting the model's capacity to learn noise. Option B (increase regularization strength) penalizes large coefficients, discouraging complex fits. Option C (collect more training data) provides more examples, helping the model generalize. Option D (increase maximum depth) increases model complexity, worsening overfitting. Option E (adding interaction features) increases complexity, likely increasing 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 selecting fewer features
Why this is correct
Simpler models generalize better.
- ✓
Increase regularization strength (e.g., L1, L2)
Why this is correct
Regularization reduces overfitting by penalizing large weights.
- ✓
Collect more training data if possible
Why this is correct
More data reduces overfitting.
- ✗
Increase the maximum depth of decision trees
Why it's wrong here
Deeper trees overfit more.
- ✗
Add more interaction features
Why it's wrong here
More features can increase overfitting.
Go deeper
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist builds a Random Forest model using SageMaker. The model performs well on training data but poorly on test data. Which step is most likely to reduce overfitting?
medium- ✓ A.Reduce the maximum depth of each tree
- B.Increase the number of trees
- C.Switch to a linear model
- D.Increase the number of features considered at each split
Why A: Reducing the maximum depth of each tree limits the complexity of individual decision trees, preventing them from memorizing noise and specific patterns in the training data. This directly addresses overfitting by enforcing simpler, more generalized splits, which improves performance on unseen test data.
JA
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Senior Network & Security Engineer · founder of Courseiva
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