!!! Overview
[{$pagename}]

!! [Machine Learning] [1]
[{$pagename}] is a [parameter] whose value is set before the learning process begins. By contrast, the values of other [parameters] are derived via training.

Different [Machine Learning model]'s training [Machine Learning Algorithms] require different [{$pagename}]. Given these hyperparameters, the training algorithm learns the [parameters] from the [data]. For instance, LASSO is an algorithm that adds a [regularization] hyperparameter to OLS regression, which has to be set before estimating the [parameters] through the training [algorithm].

Some simple algorithms (such as ordinary least squares regression) require none. 

!! [{$pagename}] Statistics [2]
Bayesian [{$pagename}] is a [parameter] of a prior distribution; the term is used to distinguish them from [parameters] of the [model] for the underlying system under analysis.

!! More Information
There might be more information for this subject on one of the following:
[{ReferringPagesPlugin before='*' after='\n' }]
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* [#1] - [Hyperparameter_(machine_learning)|Wikipedia:Hyperparameter_(machine_learning)|target='_blank'] - based on information obtained 2018-01-03
* [#2] - [Hyperparameter|Wikipedia:Hyperparameter|target='_blank'] - based on information obtained 2018-01-03