Choose a Parameter to Reduce Underfitting in Gradient-Boosted Trees
C3 AIData Scientist · Online Assessment
Last updated
Explain why decreasing minimum observations per leaf can reduce gradient-boosted tree underfitting and how to validate the change.
Choose a Parameter to Reduce Underfitting in Gradient-Boosted Trees
A gradient-boosted tree model is underfitting. Which parameter could you decrease to allow a more flexible fit: tree depth, number of leaves, the proportion of observations used to fit a tree, or the minimum number of observations per leaf? Explain why your choice can help and how you would verify the diagnosis.
Hints
Hint 1
Consider a blocked split: A useful split may be forbidden if one child contains fewer observations than the minimum leaf size.
Follow-up questions
- What risk rises when the minimum leaf size becomes very small?
- How would you distinguish underfitting from poor features or a target-definition error?
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