Interface administrators, Administrators (Semantic MediaWiki), Curators (Semantic MediaWiki), Editors (Semantic MediaWiki), Suppressors, Administrators
7,785
edits
Training loss: Difference between revisions
no edit summary
(Created page with "{{see also|Machine learning terms}} ==Introduction== Training loss is an important metric in machine learning that measures the discrepancy between predicted output and actual output. It helps evaluate a model's performance during training, with the aim being to minimize this loss so that it can generalize well on unseen data. ==Types of Loss Functions== Machine learning employs a variety of loss functions, depending on the problem being solved and the model being emplo...") |
No edit summary |
||
| Line 1: | Line 1: | ||
{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
Training loss is an important metric in machine learning that measures the discrepancy between predicted output and actual output. It helps evaluate a model's performance during training, with the aim being to minimize this loss so that it can generalize well on unseen data. | [[Training loss]] is an important [[metric]] in [[machine learning]] that measures the discrepancy between [[predicted]] [[output]] and actual output ([[label]]). It is the [[model]]'s [[loss]] during a specific [[training]] [[iteration]]. Training loss helps to evaluate a model's [[performance]] during training, with the aim being to minimize this loss so that it can generalize well on unseen data. | ||
==Types of Loss Functions== | ==Types of Loss Functions== | ||