Introduction

In this chapter, we will learn about some different evaluation techniques other than accuracy. For any data scientist, the first step after building a model is to evaluate it, and the easiest way to evaluate a model is through its accuracy. However, in real-world scenarios, we often deal with datasets where accuracy is not the best evaluation technique. This chapter explores core concepts such as imbalanced datasets and how different evaluation techniques can be used to work through these imbalanced datasets. The chapter begins with an introduction to accuracy and its limitations. It then explores the concepts of null accuracy, imbalanced datasets, sensitivity, specificity, precision, false positives, ROC curves, and AUC scores.