Introduction

In the previous chapter, we explored model evaluation in detail. We covered accuracy and why it may be misleading for some datasets, especially for classification tasks with highly imbalanced classes. Datasets with imbalanced classes such as the prediction of hurricanes in the Pacific Ocean or the prediction of whether someone will default on their credit card loan have positive instances that are relatively rare compared to negative instances, so accuracy scores are misleading since the null accuracy is so high.

To combat class imbalance, we learned about techniques that we can use to appropriately evaluate our model, including calculating model evaluation metrics such as the sensitivity, specificity, false positive rate, and AUC score, and plotting the ROC curve. In this chapter, we will learn how to classify another type of dataset—namely, images. Image classification is extremely useful and there are many real-world applications of it, as we will discover...