{"id":5021,"date":"2024-03-19T13:25:44","date_gmt":"2024-03-19T13:25:44","guid":{"rendered":"https:\/\/radicalbit.ai\/?post_type=glossary&p=5021"},"modified":"2024-04-02T10:38:14","modified_gmt":"2024-04-02T10:38:14","slug":"hyperparameter","status":"publish","type":"glossary","link":"https:\/\/radicalbit.ai\/it\/resources\/glossary\/hyperparameter\/","title":{"rendered":"Hyperparameter"},"content":{"rendered":"\n

In machine learning, hyperparameter is a parameter, such as the learning rate or choice of optimiser, which control the learning process. In contrast to normal (i.e. not hyper) parameters which are determined by the model itself during the learning phase, hyperparameters cannot be inferred while fitting the model to the training set.<\/p>\n","protected":false},"excerpt":{"rendered":"

In machine learning, hyperparameter is a parameter, such as the learning rate or choice of optimiser, which control the learning process. In contrast to normal…<\/p>\n","protected":false},"author":1,"featured_media":4814,"menu_order":0,"template":"","meta":{"footnotes":""},"class_list":["post-5021","glossary","type-glossary","status-publish","has-post-thumbnail","hentry"],"yoast_head":"\nHyperparameter | Radicalbit<\/title>\n<meta name=\"description\" content=\"Hyperparameter is a learning rate or choice of optimiser, that cannot be inferred while fitting the model to the training set.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/radicalbit.ai\/it\/resources\/glossary\/hyperparameter\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hyperparameter | Radicalbit\" \/>\n<meta property=\"og:description\" content=\"Hyperparameter is a learning rate or choice of optimiser, that cannot be inferred while fitting 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