hyperparameter vs hyper-parameter

Both 'hyperparameter' and 'hyper-parameter' are correct, but 'hyperparameter' is more commonly used in the field of machine learning and optimization. The hyphenated form 'hyper-parameter' is also acceptable, but less prevalent.

Last updated: April 13, 2024 • 156 views

This is correct and commonly used in the field of machine learning and optimization.

"hyperparameter"

The term 'hyperparameter' refers to a parameter whose value is set before the learning process begins. It is commonly used in the context of machine learning algorithms.

Examples:

Tuning hyperparameters is crucial for optimizing the performance of a machine learning model.
The choice of hyperparameters can significantly impact the model's accuracy.

Show examples from the web [+]

In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters ...
Jul 26, 2017 ... A model hyperparameter is a configuration that is external to the model and whose value cannot be estimated from data.
In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the ...
... and neural network architectures. We compute hyperparameter gradients by exactly reversing the dynamics of stochastic gradient descent with momentum.
Aug 10, 2017 ... Hyperparameter overview. But first, let's quickly recap what hyperparameters are. Hyperparameters are parameters of the training algorithm ...
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. Typically, you choose this if the range of all values from the ...
Hyperparameter tuning optimizes a single target variable, also called the hyperparameter metric, that you specify. The accuracy of the model, as calculated from ...
Hyperparameter tuning launches training jobs that use hyperparameter values in the ranges that you specified, and returns the training job with highest auc. You ...
Jan 11, 2023 ... What is hyperparameter tuning? Hyperparameters are adjustable parameters that let you control the model training process. For example, with ...

Alternatives:

parameter
variable
tunable parameter
configurable parameter
adjustable parameter

This is correct but less commonly used compared to 'hyperparameter'.

"hyper-parameter"

The hyphenated form 'hyper-parameter' is also acceptable to refer to a parameter set before the learning process in machine learning and optimization, but it is less common.

Examples:

The hyper-parameter optimization process can improve the model's performance.
Understanding the role of each hyper-parameter is essential for model development.

Show examples from the web [+]

for hyper-parameter optimization than trials on a grid. Empirical evidence comes from a compar- ison with a large previous study that used grid search and ...
We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs). We optimize hyper-parameters using random ...
Dec 12, 2011 ... We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs).
and Bengio, Y., Random search for hyper-parameter optimization, The Journal of Machine Learning Research (2012). 3.2.3. Searching for optimal parameters with ...
We present hyper-parameter optimization results on tasks of training neu- ral networks and deep belief networks (DBNs). We optimize hyper-parameters using ...
Mar 16, 2020 ... In this study, Microcanonical Optimization algorithm which is a variant of Simulated Annealing method is used for hyper-parameter ...
list-hyper-parameter-tuning-jobs is a paginated operation. Multiple API calls may be issued in order to retrieve the entire data set of results.
Sep 28, 2020 ... More specifically, we prove that when the hyper-parameter $\beta_2$ is close enough to $1$, RMSprop and its random shuffling version ...

Alternatives:

parameter
variable
tunable parameter
configurable parameter
adjustable parameter

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hyperparameter vs hyper-parameter

"hyperparameter"

"hyper-parameter"

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