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| ==en construction==
| | #REDIRECTION[[hyperparamètre]] |
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| == Définition ==
| | [[Catégorie:GRAND LEXIQUE FRANÇAIS]] |
| [DOUBLON]
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| Voir '''[[hyperparamètre]]'''
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| == Français ==
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| ''' Hyperparametre'''
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| == Anglais ==
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| ''' Hyperparameter'''
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| A hyperparameter is a machine learning parameter whose value is chosen before a learning algorithm is trained.
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| Hyperparameters should not be confused with parameters . In machine learning, the label parameter is used to identify variables whose values are learned during training. The prefix hyper is used to identify higher-level parameters that control the learning process.
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| Every variable that an AI engineer or ML engineer chooses before model training begins can be referred to as a hyperparameter -- as long as the value of the variable remains the same when training ends.
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| It’s important to choose the right hyperparameters before training begins because this type of variable has a direct impact on the performance of the resulting machine learning model. Examples of hyperparameters in machine learning include:
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| Model architectureLearning rateNumber of epochsNumber of branches in a decision treeNumber of clusters in a clustering algorithm
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| Hyperparameters may also be referred to as meta parameters.
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| The process of choosing which hyperparameters to use is called hyperparameter tuning. The process of tuning may also be referred to as hyperparameter optimization (HPO).
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| <small>
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| [https://www.techopedia.com/definition/34625/hyperparameter-ml-hyperparameter Source : techopedia]
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| [[Catégorie:vocabulary]] | |
