What Is Expected Loss and How Does High School Calculus Play Into It?

In machine learning and statistics, computing the accuracy, or loss, of a model is crucial for understanding the quality of the model and what improvements can be made to increase accuracy. Typically, researchers choose a loss function de- pending on their task, and this loss function runs over their test set of data, after training. However, in many cases, researchers want an estimation of their loss either before they test it or in cases when testing data is not yet available. This estimation is known as expected loss, or risk, and is usually utilized in order to assess how precarious an action or event will be. The foundations of Bayesian statistics are rooted in Bayes' Theorem, a theorem developed by Thomas Bayes who was an English mathematician and theologian during the 1700s.