What are the key parameters to consider when building a decision tree?
An example of an AI chatbot conversation about decision tree parameters.
What are the key parameters to consider when building a decision tree?
When building a decision tree, key parameters include maximum depth, minimum samples split, and criterion for splitting.
Can you explain what 'maximum depth' means?
Maximum depth refers to the longest path from the root node to a leaf node, controlling the tree's complexity.
What happens if the maximum depth is too high?
If the maximum depth is too high, the model may overfit the training data, leading to poor generalization on new data.
How do I choose the right minimum samples split?
The right minimum samples split depends on your dataset size; a common approach is to start with a small value and adjust based on performance.
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