How do you handle overfitting in Decision Trees by setting the max depth and min samples split parameters

Question

Can you tell me How do you handle overfitting in Decision Trees by setting the max_depth and min_samples_split parameters?

Answer 1

You can handle overfitting in Decision Trees by setting max_depth to limit tree depth and min_samples_split to control the minimum samples needed to split a node.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• max_depth=5 limits the depth of the tree, reducing complexity and overfitting risk.
• min_samples_split=10 ensures that a node must have at least 10 samples to split, preventing overfitting on small data subsets.
• random_state=42 ensures reproducibility of results.
• accuracy_score() evaluates model performance on test data.

Hence, controlling max_depth and min_samples_split balances model complexity and generalization, effectively reducing the risk of overfitting in Decision Trees. Let me know if you’d like any adjustments!