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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 hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] [3] Hyperparameter optimization determines the set of hyperparameters that yields an optimal model which minimizes a predefined loss function on a given data set. [4]
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
First, we split the full dataset into two parts: a training set and a validation set. The training set would be used to fit more and more model parameters, and the validation set would be used to decide which parameters to include, and when to stop fitting completely. The GMDH starts by considering degree-2 polynomial in 2 variables.
The Bernoulli distribution (with parameter p) is the model of the underlying system; p is a parameter of the underlying system (Bernoulli distribution); The beta distribution (with parameters α and β) is the prior distribution of p; α and β are parameters of the prior distribution (beta distribution), hence hyperparameters;
For example, if one is using a beta distribution to model the distribution of the parameter p of a Bernoulli distribution, then: p is a parameter of the underlying system (Bernoulli distribution), and; α and β are parameters of the prior distribution (beta distribution), hence hyperparameters.
In this context, and are called hyperparameters (parameters of the prior), to distinguish them from parameters of the underlying model (here ). A typical characteristic of conjugate priors is that the dimensionality of the hyperparameters is one greater than that of the parameters of the original distribution.
Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. [1] The sub-models combine to form the hierarchical model, and Bayes' theorem is used to integrate them with the observed data and account for all the ...