DA0-002 Data Analysis Practice Question
After training a decision tree, the tree has depth 20 and 100% accuracy on training data but only 60% on test data. Which hyperparameter adjustment is most likely to improve generalization?
⚠ Common exam trap
Test-takers frequently confuse hyperparameters that reduce overfitting with those that increase model complexity, mistakenly choosing options like 'increase maximum depth' or 'decrease minimum samples per split' thinking they will improve accuracy.
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
✓
Increase minimum samples per leaf
The model is overfitting: 100% training accuracy vs. 60% test accuracy with a depth-20 tree. Increasing minimum samples per leaf forces the tree to be simpler by requiring more samples in each leaf, reducing variance and improving generalization. This directly combats the overfitting caused by the overly deep tree.
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 number of estimators
Why it's wrong here
Number of estimators is a parameter for ensemble methods like Random Forest, not for a single decision tree.
- ✗
Decrease minimum samples per split
Why it's wrong here
Decreasing min_samples_split allows splits on smaller samples, increasing complexity and overfitting.
- ✓
Increase minimum samples per leaf
Why this is correct
Increasing min_samples_leaf prevents the tree from fitting noise by requiring more samples in each leaf, reducing overfitting.
- ✗
Increase maximum depth
Why it's wrong here
Increasing max_depth makes the tree deeper and more prone to overfitting, not less.
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