MLS-C01 Modeling Practice Question
A data scientist is training a Random Forest model on Amazon SageMaker. The model performs well on the training set but poorly on the test set. Which technique should the data scientist use to address this issue?
⚠ Common exam trap
AWS often tests the misconception that increasing model complexity (e.g., more trees or deeper trees) always improves performance, when in fact overfitting requires reducing complexity or applying regularization.
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
✓
Decrease the maximum depth of each tree
The model is overfitting, as indicated by high training performance and poor test performance. Decreasing the maximum depth of each tree limits the complexity of individual trees, reducing overfitting by preventing them from memorizing noise in the training data. This is a standard regularization technique for Random Forest models in Amazon SageMaker.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of trees in the forest
Why it's wrong here
Increasing the number of trees in a Random Forest generally reduces overfitting by averaging more trees, but it is not the most direct method to combat overfitting when the trees themselves are too deep. The primary issue is the complexity of individual trees; therefore, decreasing tree depth is more effective.
- ✓
Decrease the maximum depth of each tree
Why this is correct
Decreasing the maximum depth of each tree limits the complexity of individual trees, reducing overfitting by preventing them from memorizing noise. This is a standard regularization technique and directly addresses the overfitting issue.
- ✗
Increase the learning rate
Why it's wrong here
Learning rate is not a parameter of Random Forest; it is used in gradient boosting methods. Thus, this option is irrelevant.
- ✗
Increase the maximum depth of each tree
Why it's wrong here
Increasing the maximum depth of each tree exacerbates overfitting by allowing trees to memorise noise in the training data, which is the core problem here—the model already fits training well but generalises poorly. This technique is tempting because deeper trees reduce training error further, and would be correct if the model suffered from high bias (underfitting) on the training set.
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