Uncertainty quantification for regression problems

After completing this chapter, whenever you predict any continuous variable, you’ll be equipped to add a layer of robustness and reliability to your predictions. Understanding and quantifying this uncertainty is crucial for several reasons:

• Model interpretability and trust: Uncertainty quantification helps us understand the reliability of our model predictions. By providing a range of possible outcomes, we can build trust in our model’s predictions and interpret them more effectively.
• Decision-making: In many practical applications of regression analysis, decision-makers must rely on something other than point estimates. They often need to know the range within which the actual value will likely fall with a certain probability. This range, or prediction interval, provides crucial information about the uncertainty of the prediction and aids in risk management.
• Model improvement: Uncertainty can highlight...