« Théorème No Free Lunch » : différence entre les versions


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== en construction ==  
== en construction ==  


Vocabulaire
== Définition ==


== Définition ==
== Français ==
'''théorème No Free Lunch'''


'''théorème NFL'''


'''théorème « Rien n'est gratuit! »'''


== Anglais ==
'''No Free Lunch theorem'''


== Français ==
'''NFL theorem'''
'''gratuit '''


'''bonus'''
The No Free Lunch Theorem, often abbreviated as NFL or NFLT, is a theoretical finding that suggests all optimization algorithms perform equally well when their performance is averaged over all possible objective functions.


'''repas gratuit'''
There are, generally speaking, two No Free Lunch (NFL) theorems: one for machine learning and one for search and optimization. These two theorems are related and tend to be bundled into one general axiom (the folklore theorem).


TANSTAFL - there ain't no such thing as a free lunch.


[https://machinelearningmastery.com/no-free-lunch-theorem-for-machine-learning/  Source : machinelearningmastery.com ]


== Anglais ==
[https://www.kdnuggets.com/2019/09/no-free-lunch-data-science.html  Source : KDnuggets ]
'''free-lunch'''


[https://www.btb.termiumplus.gc.ca/tpv2alpha/alpha-fra.html?lang=fra&i=1&srchtxt=NO+FREE+LUNCH+THEOREM&codom2nd_wet=1#resultrecs  Source : TERMIUM Plus ]


[[Catégorie:Vocabulaire]]
[[Catégorie:Vocabulaire]]

Version du 7 septembre 2021 à 10:07

en construction

Définition

Français

théorème No Free Lunch

théorème NFL

théorème « Rien n'est gratuit! »

Anglais

No Free Lunch theorem

NFL theorem

The No Free Lunch Theorem, often abbreviated as NFL or NFLT, is a theoretical finding that suggests all optimization algorithms perform equally well when their performance is averaged over all possible objective functions.

There are, generally speaking, two No Free Lunch (NFL) theorems: one for machine learning and one for search and optimization. These two theorems are related and tend to be bundled into one general axiom (the folklore theorem).

TANSTAFL - there ain't no such thing as a free lunch.

Source : machinelearningmastery.com

Source : KDnuggets

Source : TERMIUM Plus

Contributeurs: Jean Benoît Morel, wiki