CHAPTER 7 – Performance Metrics

Publisher Summary This chapter explores a few issues related to measuring how well a neural network tool is doing. It is not a subject that has been treated extensively in the literature; therefore, in few cases, the techniques that have been applied in related areas are adapted to measuring performance. The chapter reviews a number of issues related to measuring neural network tool performance. It discusses the selection of the gold standards against which performance is measured and the role that the decision threshold level can play in determining system performance. The performance measurements discussed in the chapter include the relatively simple measure of the percent correct, the average sum-squared error measure, receiver operating characteristic(ROC) curve measurements, measurements based on ROC curve parameters, which are recall, precision, sensitivity, specificity, etc., and the chi-square goodness-of-fit metric. The specific measure chosen depends on the type of system that is being used and on other, somewhat more loosely defined parameters such as the level of technical sophistication of the system end user.