MLS-C01 Modeling Practice Question
Which TWO actions can help reduce overfitting in a decision tree model? (Choose 2.)
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that increasing model complexity (e.g., deeper trees or more features) always improves accuracy, when in fact it increases overfitting; candidates may incorrectly select options that add complexity instead of 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
✓
Prune the tree after training
Pruning the tree after training removes branches that have little predictive power, reducing overfitting by simplifying the model. This technique directly addresses the variance component of the bias-variance tradeoff, making the model generalize better to unseen data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Prune the tree after training
Why this is correct
Pruning removes branches that have little predictive power, reducing overfitting.
- ✗
Increase the maximum depth of the tree
Why it's wrong here
Increasing depth leads to more overfitting.
- ✓
Set a minimum number of samples per leaf
Why this is correct
Requiring more samples per leaf prevents the tree from learning noise.
- ✗
Increase the number of features considered at each split
Why it's wrong here
More features can increase overfitting.
- ✗
Use all training data without validation
Why it's wrong here
This does not address overfitting.
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