hyperparameter
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[edit]Etymology
[edit]Noun
[edit]hyperparameter (plural hyperparameters)
- (Bayesian statistics) A parameter of a prior (as distinguished from inferred parameters of the model for the underlying system under analysis).
- Synonym: superparameter
- Coordinate terms: metaparameter, parameter, subparameter
- this model has four hyperparameters
- 2003, Gary Koop, Bayesian Econometrics, John Wiley & Sons Ltd., page 19:
- The exact interpretation of these hyperparameters becomes clearer once you have seen their role in the posterior and, hence, we defer a deeper discussion of prior elicitation until the next section.
- (machine learning) A parameter whose value is set before the learning process begins.
- Coordinate terms: metaparameter, parameter, subparameter
- 2016, Ian Goodfellow, Yoshua Bengio, Aaron Courville, chapter 11, in Deep Learning, MIT Press, →ISBN, page 420:
- The ideal learning algorithm just takes a dataset and outputs a function, without requiring hand tuning of hyperparameters.
- 2024 May 4, Alex Hern, Dan Milmo, quoting James Betker, “Danger and opportunity for news industry as AI woos it for vital human-written copy”, in The Guardian[1], →ISSN:
- “Model behaviour is not determined by architecture, hyperparameters, or optimizer choices,” he said, referring to the technical difficulties of training a language model. “It’s determined by your dataset, nothing else. […] ”
Derived terms
[edit]Translations
[edit]in Bayesian statistics, a parameter of a prior
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See also
[edit]Further reading
[edit]- hyperparameter on Wikipedia.Wikipedia
- hyperparameter (machine learning) on Wikipedia.Wikipedia