Spoilers

What to expect from this chapter

In this chapter, we will:

• Build a model for binary classification.

• Understand the concept of logits and how it is related to probabilities.

• Use binary cross-entropy loss to train a model.

• Use the loss function to handle imbalanced datasets.

• Understand the concepts of decision boundary and separability.

• Learn how the choice of a classification threshold impacts evaluation metrics.

• Build ROC and precision-recall curves.

Imports

For the sake of organization, all libraries needed throughout the code and used in any given chapter are imported at the very beginning. For this chapter, we will need the following imports: