Model Evaluation
Abstract
This chapter describes three commonly used tools for evaluating the performance of a classification algorithm. We first introduce the confusion matrix and provide the definitions for several terms that are used in conjunction, such as sensitivity, specificity, recall, etc. We then describe how to construct receiver operating characteristic (ROC) curves and show when it would be appropriate to use them along with the area under the curve (AUC) concept. Finally we present lift and gain charts, and show how to construct and interpret them. The RapidMiner implementation includes step-by-step processes for building each of these three very useful evaluation tools.
Keywords
Model evaluation; classification performance; ...Get Predictive Analytics and Data Mining now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
