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  2. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    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] Hyperparameter optimization determines the set of ...

  3. Hyperparameter (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_(machine...

    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).

  4. Model selection - Wikipedia

    en.wikipedia.org/wiki/Model_selection

    Model selection may also refer to the problem of selecting a few representative models from a large set of computational models for the purpose of decision making or optimization under uncertainty. [2] In machine learning, algorithmic approaches to model selection include feature selection, hyperparameter optimization, and statistical learning ...

  5. Bayesian optimization - Wikipedia

    en.wikipedia.org/wiki/Bayesian_optimization

    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.

  6. k-nearest neighbors algorithm - Wikipedia

    en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

    In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Hodges in 1951, [1] and later expanded by Thomas Cover. [2] Most often, it is used for classification, as a k-NN classifier, the output of which is a class membership.

  7. Learning rate - Wikipedia

    en.wikipedia.org/wiki/Learning_rate

    In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. [1]

  8. Automated machine learning - Wikipedia

    en.wikipedia.org/wiki/Automated_machine_learning

    Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. [4] Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search.

  9. Hyperparameter (Bayesian statistics) - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_(Bayesian...

    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: