What is pruning in decision trees?
An example of an AI chatbot conversation about pruning decision trees in machine learning.
What is pruning in decision trees?
Pruning in decision trees is a technique used to reduce the size of the tree by removing sections that provide little power in predicting target variables.
Why is pruning important?
Pruning helps to prevent overfitting, making the model more generalizable to unseen data, which improves its performance.
What are the different methods of pruning?
Common methods include pre-pruning, where you stop the tree from growing too deep, and post-pruning, where you remove branches after the tree is fully grown.
Can you give an example of post-pruning?
Sure! In post-pruning, you might use a validation set to evaluate the performance of the tree and remove branches that do not improve accuracy.
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