Summary
In this chapter, we have introduced conformal prediction and explained the multiple benefits of this powerful framework for reliably quantifying the uncertainty of predictions to improve trust in machine learning models.
We explained that the key benefit of conformal prediction is that, unlike any other probabilistic prediction framework, it provides valid probabilistic predictions accompanied by confidence measures, regardless of the underlying model, the dataset size, and the data distribution.
We then explored the origins of conformal prediction and saw how it has recently become a very popular framework adopted by leading universities and companies.
Finally, we looked at how conformal prediction differs from traditional machine learning and learned about the role of p-values in conformal prediction.
In Chapter 2, we will explain why conformal prediction is a valuable tool for quantifying the uncertainty of predictions, especially in critical settings such as...
