A comparison of pruning criteria for probability trees

摘要

Probability trees are decision trees that predict class probabilities rather than the most likely class. The pruning criterion used to learn a probability tree strongly influences the size of the tree and thereby also the quality of its probability estimates. While the effect of pruning criteria on classification accuracy is well-studied, only recently has there been more interest in the effect on probability estimates. Hence, it is currently unclear which pruning criteria for probability trees are preferable under which circumstances.