6. Model Evaluation

Overview

This chapter covers model evaluation in depth. We will discuss alternatives to accuracy to evaluate the performance of a model when standard techniques are not feasible, especially where there are imbalanced classes. Finally, we will utilize confusion matrices, sensitivity, specificity, precision, FPR, ROC curves, and AUC scores to evaluate the performance of classifiers. By the end of this chapter, you will have an in-depth understanding of accuracy and null accuracy and will be able to understand and combat the challenges of imbalanced datasets.