MLS-C01 Modeling Practice Question
A data scientist is using a decision tree algorithm for a classification task. The tree is very deep and achieves 100% accuracy on the training set but performs poorly on the test set. Which technique should the data scientist use to improve generalization?
⚠ Common exam trap
It's easy for candidates to confuse overfitting with underfitting and choose to increase model complexity (Option D) or add features (Option A), when the correct remedy for overfitting is to reduce complexity through pruning.
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 decision tree.
A deep decision tree that achieves 100% training accuracy but poor test accuracy is overfitting the training data. Pruning the tree removes branches that have little statistical power, reducing complexity and improving generalization 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.
- ✗
Add more features to the dataset.
Why it's wrong here
More features can lead to overfitting if not regularized.
- ✗
Reduce the number of training samples.
Why it's wrong here
Reducing data typically worsens performance and increases overfitting risk.
- ✓
Prune the decision tree.
Why this is correct
Pruning reduces tree complexity and improves generalization.
- ✗
Increase the maximum depth of the tree.
Why it's wrong here
Increasing depth increases overfitting.
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