Costs are Too High Even the best tree incurs unacceptably high error rates.
If some misclassifications are worse than others, you might be able to improve CART performance by specifying variable misclassification costs. While this method cannot improve the overall unweighted error rate of the tree, it can improve performance for the most important classes. The tools within CART are designed to give you the most reliable assessment of the predictive accuracy of your tree. If the error rate of the optimal tree is too high, CART is telling you that you probably cannot honestly get what you want. An objective assessment of your data reveals poor predictive power for virtually any model.