Model Evaluation
Vijay Kotu, Bala Deshpande · Data Science · 2019
This chapter describes three commonly used tools for evaluating the performance of a classification algorithm. The confusion matrix will first need to be introduced and the definitions for several terms that are used in conjunction will be provided, such as sensitivity, specificity, recall, etc. How to construct receiver operating characteristic curves will then be described and when it would be appropriate to use them along with the area under the curve concept will be shown. Finally, lift and gain charts are presented, and how to construct and interpret them is shown. The RapidMiner implementation includes step-by-step processes for building each of these three very useful evaluation tools.