« Model Parameters » : différence entre les versions

Dernière version du 3 janvier 2022 à 15:55

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-==en construction==
+#redirection [[paramètre de modèle]]'''
-== Définition ==
+[[Catégorie:ENGLISH]]
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-== Français ==
-''' XXXXXXXXX '''
-== Anglais ==
-''' Model Parameters'''
-In a machine learning model, there are two types of parameters:
-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.
-b) Hyperparameters: These are adjustable parameters that must be tuned to obtain a model with optimal performance.
-It is important that during training, the hyperparameters be tuned to obtain the model with the best performance (with the best-fitted parameters).
-<small>
-[https://www.kdnuggets.com/2020/12/20-core-data-science-concepts-beginners.html  Source : kdnuggets]
-[[Catégorie:vocabulary]]