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