July 2020
Intermediate to advanced
496 pages
9h 10m
English
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.
In the previous chapter, we covered regularization techniques for neural networks. Regularization is an important technique when it comes to combatting ...