Conformal Prediction for Regression

In this chapter, we will cover conformal prediction for regression problems.

Regression is a cornerstone of machine learning, enabling us to predict continuous outcomes from given data. However, as with many predictive tasks, the predictions are never free from uncertainty. Traditional regression techniques give us a point estimate but fail to measure the uncertainty. This is where the power of conformal prediction comes into play, extending our regression models to produce well-calibrated prediction intervals.

This chapter delves deep into conformal prediction tailored specifically for regression problems. By understanding and appreciating the importance of quantifying uncertainty, we will explore how conformal prediction augments regression to provide not just a point prediction but an entire interval or even a distribution where the actual outcome will likely fall with pre-specified confidence. This is invaluable in many real-world scenarios...