Hyperparameter means: A parameter of a prior (as distinguished from inferred parameters of the model for the underlying system under analysis). Below you'll find 3 example sentences showing how to use Hyperparameter in practice.
Hyperparameter in a sentence
Related words
Hyperparameter meaning
- A parameter of a prior (as distinguished from inferred parameters of the model for the underlying system under analysis).
- A parameter whose value is set before the learning process begins.
Example types with hyperparameter
The same corpus examples are grouped by length and sentence type, making it easier to see the contexts in which the word appears:
Advanced aspects like hyperparameter tuning, regularization, and cross-validation are crucial for optimizing the model’s ability to generalize to unseen data. (22 words)
Note that each of the priors has a hyperparameter specifying the number of pseudo-observations, and in each case this controls the relative variance of that prior. (27 words)
Since it has the form of a gamma pdf, this can easily be filled in, and one obtains: : Here the hyperparameter α can be interpreted as the number of prior observations, and β as the sum of the prior observations. (40 words)
Since it has the form of a gamma pdf, this can easily be filled in, and one obtains: : Here the hyperparameter α can be interpreted as the number of prior observations, and β as the sum of the prior observations. (40 words)
Note that each of the priors has a hyperparameter specifying the number of pseudo-observations, and in each case this controls the relative variance of that prior. (27 words)
Advanced aspects like hyperparameter tuning, regularization, and cross-validation are crucial for optimizing the model’s ability to generalize to unseen data. (22 words)
Example sentences (3)
Advanced aspects like hyperparameter tuning, regularization, and cross-validation are crucial for optimizing the model’s ability to generalize to unseen data.
Note that each of the priors has a hyperparameter specifying the number of pseudo-observations, and in each case this controls the relative variance of that prior.
Since it has the form of a gamma pdf, this can easily be filled in, and one obtains: : Here the hyperparameter α can be interpreted as the number of prior observations, and β as the sum of the prior observations.