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A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] Hyperparameter optimization determines the set of hyperparameters that yields an optimal model which minimizes a predefined loss function on a given data set. [3]
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).
A set of PowerToys for Windows Media Player was released as part of the Windows Media Player Bonus Pack (for Windows XP), consisting of five tools to "provide a variety of enhancements to Windows Media Player." [41] [42] Finally, Microsoft has also released PowerToys for Windows Mobile, Visual Studio [43] [44] [45] and OneNote. [46] [47]
Bayesian optimization of a function (black) with Gaussian processes (purple). Three acquisition functions (blue) are shown at the bottom. [8]Bayesian optimization is typically used on problems of the form (), where is a set of points, , which rely upon less (or equal to) than 20 dimensions (,), and whose membership can easily be evaluated.
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 underlying system under analysis. For example, if one is using a beta distribution to model the distribution of the parameter p of a Bernoulli distribution , then:
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English: In hyperparameter optimization with tree-structured Parzen estimators (TPE), the optimizer creates a model of the relation between hyperparameters and measured performance of the machine learning model to optimize. Areas of the search space with a better performance are searched more likely.