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Loss function

In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function…

Examples, Selecting a loss function & Overview

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Constructing loss and objective functions

Expected loss

Decision rules

Selecting a loss function

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Loss function

Nodes92
Edges91
Triples70
Avg. degree1.98
Density0.021739
Components1

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Loss function

Top relations

related to Further reading · 29
Loss function → April, Aretz, Asymmetric Loss Functions, Bartram, Bayesian Analysis, Berger, Bibcode, Cecchetti, Economic Policy, Expected Stock Returns, Forecasting, International Journal, ISBN, James, June, Kevin, Making, MR, New York, Objectives
related to Constructing loss and objective functions · 10
Loss function → Among, Andranik Tangian, European, German, He, In, Nobel Prize, Ragnar Frisch, The, Westfalian
related to Decision rules · 5
Loss function → Choose, Invariance, Minimax, Some, Theta
related to Quadratic loss function · 5
Loss function → If, It, SEL, The, This
related to Selecting a loss function · 4
Loss function → Sound, Still, Thus, Under
related to Regret · 3
Loss function → Bayesian, Leonard, Savage
related to 0-1 loss function · 2
Loss function → Hamming, In
related to Statistics · 2
Loss function → Bayesian, Both
is a · 1
Loss function → 0-1 loss function L
related to Expected loss · 1
Loss function → In

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Important terminology

loss function decision displaystyle functions quadratic expected risk value used example objective statistics theta rule utility also error problem data

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Loss functionis a0-1 loss function L0.90text
minimaxinstance ofSavage argued that using non-Bayesian methods0.80text
the loss function should be based on the idea of regretinstance ofSavage argued that using non-Bayesian methods0.80text
i.e.instance ofSavage argued that using non-Bayesian methods0.80text
the loss associated with a decision should be the difference between the consequences of the best decision that could have been made under circumstances will be knowninstance ofSavage argued that using non-Bayesian methods0.80text
the decision that was in fact taken before they were known.Quadratic loss functionThe use of a quadratic loss function is commoninstance ofSavage argued that using non-Bayesian methods0.80text
for example when using least squares techniquesinstance ofSavage argued that using non-Bayesian methods0.80text
the decision that was in fact taken before they were knowninstance ofSavage argued that using non-Bayesian methods0.80text
Loss functionrelated to 0-1 loss functionIn0.60section
Loss functionrelated to 0-1 loss functionHamming0.60section
Loss functionrelated to Constructing loss and objective functionsIn0.60section
Loss functionrelated to Constructing loss and objective functionsRagnar Frisch0.60section

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