This section first explains why a decision tree is the natural form of knowledge representation for classification with expensive tests. It then discusses how we measure the average cost of classification of a decision tree. Our method for measuring average cost handles aspects of the problem that are typically ignored. The method can be applied to any standard classification decision tree, regardless of how the tree is generated. We end with a discussion of the relation between cost and accuracy.