Overfitting of decision tree and tree pruning, How to avoid overfitting in data mining

By: Prof. Dr. Fazal Rehman | Last updated: March 3, 2022

Overfitting of tree

Before overfitting of the tree, let’s revise test data and training data; Training Data: Training data is the data that is used for prediction. Test Data: Test data is used to assess the power of training data in prediction. Overfitting: 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;

1. Pre-pruning
2. Post-pruning

1.Pree-Pruning: Pree-Pruning means to stop the growing tree before a tree is fully grown. 2. Post-Pruning: 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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