Hyperparameter optimization: Difference between revisions

Revision as of 17:59, 21 September 2024

Revision as of 05:47, 23 September 2024

Line 1:

{{Short description|MachineThe process of finding the optimal set of hyperparameters for a machine learning problemalgorithm}}

In [[machine learning]], '''hyperparameter optimization'''<ref>Matthias Feurer and Frank Hutter. [https://link.springer.com/content/pdf/10.1007%2F978-3-030-05318-5_1.pdf Hyperparameter optimization]. In: ''AutoML: Methods, Systems, Challenges'', pages 3–38.</ref> or tuning is the problem of choosing a set of optimal [[Hyperparameter (machine learning)|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.<ref>{{cite journal |last1=Yang|first1=Li|title=On hyperparameter optimization of machine learning algorithms: Theory and practice|journal=Neurocomputing|year=2020|volume=415|pages=295-316|doi=10.1016/j.neucom.2020.07.061}}</ref>