Summary
This chapter explored uncertainty quantification for regression problems, a critical aspect of data science and machine learning. It highlighted the importance of uncertainty and the methods to handle it effectively to make more reliable predictions and decisions.
One of the significant sections of this chapter was dedicated to various approaches that can be used to produce prediction intervals. It systematically broke down and explained diverse methods, elucidating how each works and their advantages and disadvantages. This detailed analysis aids in understanding the mechanisms behind these approaches and their practical application in real-world regression problems.
Furthermore, this chapter discussed building prediction intervals and predictive distributions using conformal prediction. We provided a step-by-step guide to constructing these intervals and distributions. This chapter also offered practical insights and tips for effectively utilizing conformal prediction...
