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| == Définition ==
| | #redirection [[paramètre de modèle]]''' |
| [doublon]
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| Voir '''[[paramètre de modèle]]'''
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| == Français ==
| | [[Catégorie:publication]] |
| '''paramètre de modèle'''
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| == Anglais ==
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| ''' Model Parameters'''
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| '''Model hyperparameters'''
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| In a machine learning model, there are two types of parameters:
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| a) Model Parameters: These are the parameters in the model that must be determined using the training data set. These are the fitted parameters. For example, suppose we have a model such as house price = a + b*(age) + c*(size), to estimate the cost of houses based on the age of the house and its size (square foot), then a, b, and c will be our model or fitted parameters.
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| b) Hyperparameters: These are adjustable parameters that must be tuned to obtain a model with optimal performance.
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| It is important that during training, the hyperparameters be tuned to obtain the model with the best performance (with the best-fitted parameters).
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| <small>
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| [https://www.kdnuggets.com/2020/12/20-core-data-science-concepts-beginners.html Source : kdnuggets]
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| [[Catégorie:vocabulary]] | |
