> For the complete documentation index, see [llms.txt](https://docs.nannyml.com/cloud/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.nannyml.com/cloud/v0.21.0/product-tour/model-side-panel/model-settings/performance-settings.md).

# Performance settings

Here, you can select and configure the performance metrics you want to monitor. The metrics will either be calculated and/or estimated depending on the selected performance types. Calculating metrics and thus measuring realized performance is only possible if targets are supplied.

<figure><img src="https://1673962307-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fvipr4qR9MrP243sDPhAQ%2Fuploads%2FTk65dCL3igidlqdGkhl6%2FScreenshot%202024-07-10%20at%2015.45.51.png?alt=media&amp;token=c4738c55-c5c4-4470-9b81-864d24ebdf50" alt=""><figcaption></figcaption></figure>

To estimate or calculate business value, a cost/benefit has to be assigned to each component of the confusion matrix. For example, a true positive prediction earns X, and a false positive prediction costs Y.

<figure><img src="https://1673962307-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fvipr4qR9MrP243sDPhAQ%2Fuploads%2F6eSUVfv95vlJD9Cns0bE%2FScreenshot%202024-07-10%20at%2015.55.59.png?alt=media&amp;token=aebce13f-76ac-46cf-8c8d-ab0bedbdf9dd" alt=""><figcaption></figcaption></figure>

NannyML automatically extracts thresholds based on the supplied reference data, but it is possible to configure a custom threshold here. By default, the thresholds are applied across all segments unless specified otherwise.

<figure><img src="https://1673962307-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fvipr4qR9MrP243sDPhAQ%2Fuploads%2FIBZhrUjHSsU0BTItRVT3%2FScreenshot%202024-07-10%20at%2015.52.44.png?alt=media&amp;token=f9fa4ef7-9752-4a65-a4df-4302679c8581" alt=""><figcaption></figcaption></figure>

There are two types of threshold constants and standard deviation-based thresholds:&#x20;

<figure><img src="https://1673962307-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fvipr4qR9MrP243sDPhAQ%2Fuploads%2FQx6ctTtrlK5dX5IqWTfc%2FScreenshot%202024-07-10%20at%2015.54.50.png?alt=media&amp;token=fe5c33d2-eede-4b96-9587-6f11059fa7c6" alt=""><figcaption></figcaption></figure>