Hyperparameter optimization: Difference between revisions

Revision as of 16:35, 4 January 2024

Revision as of 08:06, 12 June 2024

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=== Grid search ===

The traditional way of performing hyperparameter optimization has been ''grid search'', or a ''parameter sweep'', which is simply an [[Brute-force search|exhaustive searching]] through a manually specified subset of the hyperparameter space of a learning algorithm. A grid search algorithm must be guided by some performance metric, typically measured by [[Cross-validation (statistics)|cross-validation]] on the training set<ref>Chin-Wei Hsu, Chih-Chung Chang and Chih-Jen Lin (2010). [http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf A practical guide to support vector classification]. Technical Report, [[National Taiwan University]].</ref>

or evaluation on a hold-out validation set.<ref>{{cite journal

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