MLS-C01 Modeling Practice Question
A data scientist is training a random forest model. During hyperparameter tuning, which parameter is MOST effective at reducing overfitting?
⚠ Common exam trap
AWS often tests the misconception that adding more trees always reduces overfitting, but the trap is that while more trees stabilize predictions, they do not address the root cause of overfitting from overly complex individual trees.
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
Decreasing the maximum depth of each tree limits the complexity of individual trees, preventing them from memorizing noise and outliers in the training data. This directly reduces overfitting by enforcing simpler decision boundaries, which is a core regularization technique for ensemble methods like Random Forest.
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
Why it's wrong here
More trees can reduce overfitting but not as directly as depth.
- ✗
Increase the number of features considered per split
Why it's wrong here
More features may lead to overfitting.
- ✓
Decrease the maximum depth of each tree
Why this is correct
Shallow trees generalize better.
- ✗
Increase the maximum depth of each tree
Why it's wrong here
Deeper trees increase overfitting.
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