Summary

In this chapter, we have provided an overview of conformal prediction and explained why conformal prediction is a valuable tool for quantifying the uncertainty of predictions, especially in critical settings such as healthcare, self-driving cars, and finance. We also discussed the concept of UQ and how the conformal prediction framework has successfully addressed the challenge of quantifying uncertainty.

In the next chapter, we will dive deeper into the fundamentals of conformal prediction and apply it to binary classification problems. We will illustrate how you can apply conformal prediction to your own binary classification problems by computing non conformity scores and p-values and then using the p-values to decide which class labels should be included in your prediction sets.