MLS-C01 Modeling Practice Question
Which THREE of the following are appropriate methods to reduce overfitting in a decision tree model?
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
To reduce overfitting in a decision tree, we aim to decrease model complexity. Pruning the tree after training (C) removes branches that have little predictive power, thus simplifying the model. Setting a minimum number of samples required to split an internal node (D) prevents the tree from learning overly specific patterns from small subsets. Limiting the maximum depth of the tree (E) restricts the number of splits, reducing complexity. On the other hand, increasing the number of features considered for each split (A) can make the tree more prone to overfitting by including more irrelevant features, and increasing the maximum depth (B) allows the tree to grow deeper and capture noise, both of which increase overfitting.
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 features considered for each split
Why it's wrong here
Increasing the number of features considered for each split reduces the randomness of the splits, which can actually increase overfitting. It does not help reduce overfitting and may exacerbate it.
- ✗
Increase the maximum depth of the tree
Why it's wrong here
Increasing the maximum depth of the tree allows the tree to grow deeper, capturing more noise and leading to overfitting. This is not a method to reduce overfitting.
- ✓
Prune the tree after training
Why this is correct
Pruning the tree after training removes branches that have little predictive power, reducing complexity and helping to generalize better, which reduces overfitting.
- ✓
Set a minimum number of samples required to split an internal node
Why this is correct
Setting a minimum number of samples required to split an internal node prevents the tree from splitting on very small subsets, which often represent noise, thus reducing overfitting.
- ✓
Limit the maximum depth of the tree
Why this is correct
Limiting the maximum depth of the tree restricts the tree from growing too deep, preventing it from learning overly specific patterns in the training data, which reduces overfitting.
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