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In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] [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).
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:
The parameter is called the hyperparameter, while its distribution given by (,) is an example of a hyperprior distribution. The notation of the distribution of Y changes as another parameter is added, i.e. Y ∣ θ , μ ∼ N ( θ , 1 ) {\displaystyle Y\mid \theta ,\mu \sim N(\theta ,1)} .
Firstly, use of a hyperprior allows one to express uncertainty in a hyperparameter: taking a fixed prior is an assumption, varying a hyperparameter of the prior allows one to do sensitivity analysis on this assumption, and taking a distribution on this hyperparameter allows one to express uncertainty in this assumption: "assume that the prior is of this form (this parametric family), but that ...
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]
Hyperparameter may refer to: Hyperparameter (machine learning) Hyperparameter (Bayesian statistics) This page was last edited on 5 October 2024, at 04:17 (UTC). Text ...
In turn, the parameters can be considered samples drawn from a population characterised by hyperparameters according to a probability distribution (). In the hierarchical Bayes model, though not in the empirical Bayes approximation, the hyperparameters η {\displaystyle \eta \,} are considered to be drawn from an unparameterized distribution p ...