Overfitting of tree
Before overfitting of the tree, let’s revise test data and training data;
Training data is the data that is used for prediction.
Test data is used to assess the power of training data in prediction.
Overfitting means too many un-necessary branches in the tree. Overfitting results in different kind of anomalies that are the results of outliers and noise.[quads id=1]
How to avoid overfitting?
There are two techniques to avoid overfitting;
Pree-Pruning means to stop the growing tree before a tree is fully grown.
Post-Pruning means to allow the tree to grow with no size limit. After tree completion starts to prune the tree.
Advantages of tree-pruning and post-pruning:
- Pruning controls to increase tree un-necessary.
- Pruning reduces the complexity of the tree.
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