MLS-C01 Modeling Practice Question
A data scientist is training a decision tree classifier and notices that the model performs well on training data but poorly on test data. Which technique should the data scientist use to address this issue?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that regularization techniques like L1/L2 apply universally, when in fact they are specific to models with learnable weights (e.g., linear regression, neural networks) and not to tree-based models.
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
Pruning the tree reduces overfitting by removing branches that have little statistical significance or that capture noise in the training data. This technique improves generalization to unseen test data, which directly addresses the symptom of high training accuracy and low test accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a different split criterion
Why it's wrong here
Changing criterion may not fix overfitting.
- ✓
Prune the tree
Why this is correct
Pruning reduces overfitting.
- ✗
Apply L1 regularization
Why it's wrong here
L1 is not typical for decision trees.
- ✗
Increase tree depth
Why it's wrong here
Increasing depth worsens overfitting.
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