Review Loss Function

Loss is the value a model tries to reduce during training because it measures how wrong the current output is.

A loss function converts the difference between a model’s output and the desired result into a value the training process can minimize. Different losses encode different kinds of error.

The loss function tells the optimizer what to reward and penalize. A poorly chosen loss can improve the training number while producing behavior that misses the real goal.

A classifier uses cross-entropy loss so confident wrong predictions receive a larger penalty than correct predictions.

Loss is not the same as a business outcome or an evaluation metric. It is an optimization signal, and a lower training loss does not guarantee better real-world performance.

The loss function turns error into the signal training follows.

* [[training|Training]] * [[ml-target|ML Target]] * [[evaluation-metric|Evaluation Metric]] * [[parameters|Parameters]]

* [Google for Developers, “Machine Learning Glossary”](https://developers.google.com/machine-learning/glossary) — provides established boundaries for core machine-learning vocabulary.

* Compare [[training|Training]], [[ml-target|ML Target]], [[evaluation-metric|Evaluation Metric]], and [[parameters|Parameters]] to place Loss Function in the surrounding system. * Follow the cited source material above for the method, standard, or research context behind this draft.