Last modified on November 10th, 2019
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.
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.
Next Similar Tutorials
- What Is AssignCode and How Can It Help With Java Assignment? - May 27, 2021
- How to write a resume for a job search: Tips for applicants - May 11, 2021
- The Best Tool to Test the Speed of Your Internet - May 5, 2021