loss function

(1.5 hours to learn)


A loss function or cost function is a function that maps the outcome of a decision to a real-valued cost associated with that outcome. Loss functions are common in machine learning, information theory, statistics, and mathematical optimization, and help guide decision making under uncertainty.


-this concept has no prerequisites-

Core resources (read/watch one of the following)


Part II Decision Theory Lecture Notes
Location: pages 40-45
Author: Liam Paninski
Other notes:
  • working though the exercises is very helpful but not essential

Supplemental resources (the following are optional, but you may find them useful)


Other notes:
  • read the "Introduction" and "Use in Statistics" sections


See also

  • In Bayesian decision theory we perform inference to minimize the [posterior expected loss](posterior_expected_loss) using various loss functions. Some important results are: