A Post-Pruning Decision Tree Algorithm Based on Bayesian

The C4.5 Algorithm can result in a thriving decision tree and will overfit the training data while training the model. In order to overcome those disadvantages, this paper proposed a post-pruning decision tree algorithm based on Bayesian theory, in which each branch of the decision tree generated by the C4.5 algorithm is validated by Bayesian theorem, and then those branches that do not meet the conditions will be removed from the decision tree, at last a simple decision tree will be generated. The proposed algorithm can be verified by the data provided by the Beijing key disciplines platform and the Beijing Master and Dr. Platform. The result shows that the algorithm can the most unreliable and uneven branches. And compared with the C4.5 algorithm, the proposed algorithm has a higher prediction accuracy and a broader coverage.