How do I choose the right parameters for decision trees?
An example of an AI chatbot conversation about selecting parameters for decision trees.
How do I choose the right parameters for decision trees?
Choosing the right parameters for decision trees involves understanding key aspects like depth, min samples split, and criteria.
What does 'max depth' mean in decision trees?
'Max depth' limits how deep the tree can grow, helping to prevent overfitting by controlling complexity.
How can I determine the optimal 'min samples split' value?
You can use cross-validation to test different values for 'min samples split' and choose the one that minimizes error.
Are there any specific criteria I should consider for classification tasks?
For classification tasks, common criteria include 'gini impurity' and 'entropy', which help measure the quality of splits.
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