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Costs are Too High


Even the best tree incurs unacceptably high error rates.


SOLUTION 1: Specify Costs

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.


SOLUTION 2: Obtain More Data

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. The solution is to obtain more data on new variables.


SOLUTION 3: Create More Variables

Creating new variables such as sums, differences and ratios of your raw variables could help. You can use Salford Systems BASIC to create these new variables from within CART.


SOLUTION 4: Try Linear Combinations

Search for linear combinations with a high deletion threshold, as in:

LINEAR N=number, DELETE=.4


Steinberg, Dan and Phillip Colla. CART--Classification and Regression Trees. San Diego, CA: Salford Systems, 1997.