Applied Deep Learning with Keras

Chapter 6

Model Evaluation

Learning Objectives

By the end of this chapter, you will be able to:

• Explain model evaluation, accuracy, null accuracy, and the limitations of accuracy
• Explain imbalanced datasets and confusion matrices
• Evaluate sensitivity, specificity, precision, FPR, ROC curves, and AUC scores
• Evaluate the classification threshold

In this chapter, we will learn how to evaluate a model using accuracy. We will evaluate the model with sensitivity, specificity, precision, FPR, ROC curves, and AUC curves. Lastly, we will apply a classification threshold on the model.

Introduction

In this chapter, we will learn about some different evaluation techniques other than accuracy. For any data scientist, the first step after building ...