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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.
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:
English: For both hyperparameters of a model, a discrete set of values to search is defined (here, 10 values). In hyperparameter optimization with grid search, the model is trained using each combination of hyperparameter values (100 trials in this example) and the model performance (colored lines, better performance = blue) is saved.
Print/export Download as PDF; Printable version; In other projects ... Hyperparameter optimization; I. Incompatibility of quantum measurements;
A simple model of health deterioration after developing lung cancer could include the two parameters gender [2] and smoker/non-smoker, in which case the parameter space is the following set of four possibilities: {(Male, Smoker), (Male, Non-smoker), (Female, Smoker), (Female, Non-smoker)} .
English: In hyperparameter optimization with random search, the model is trained with randomly chosen hyperparameter values. The performance in relation to hyperparameters (colored lines, better performance = blue) does not influence the choice of trials.
Coordinate descent is an optimization algorithm that successively minimizes along coordinate directions to find the minimum of a function.At each iteration, the algorithm determines a coordinate or coordinate block via a coordinate selection rule, then exactly or inexactly minimizes over the corresponding coordinate hyperplane while fixing all other coordinates or coordinate blocks.
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