Abstract
Decision tree models typically give good classification decisions but poor probability estimates. In many applications, it is important to have good probability estimates as well. This chapter introduces a new algorithm, Bagged Lazy Option Trees (B-LOTs), for constructing decision trees and compares it to an alternative, Bagged Probability Estimation Trees (B-PETs). The quality of the class probability estimates produced by the two methods is evaluated in two ways. First, we compare the ability of the two methods to make good classification decisions when the misclassification costs are asymmetric. Second, we compare the absolute accuracy of the estimates themselves. The experiments show that B-LOTs produce better decisions and more accurate probability estimates than B-PETs.
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