What are the key parameters to consider when building decision trees?
The key parameters include max depth, min samples split, min samples leaf, and criterion for splitting.
Can you explain what 'max depth' means?
Max depth limits how deep the tree can grow, preventing overfitting by controlling complexity.
What about 'min samples split'? Why is it important?
Min samples split is the minimum number of samples required to split an internal node, helping to ensure that splits are meaningful.
How do these parameters affect the model's performance?
Adjusting these parameters can significantly impact the model's accuracy, generalization, and risk of overfitting.
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