> 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/v0.20.2/product-tour/model-side-panel/model-settings.md).

# Model settings

Under model settings, you can find all the monitoring parameters of a selected model. These settings are specific to a single model and are not confused with the general NannyML settings in the navbar.

On the left side, you can navigate through the different configuration groupings. There is also a "Run now" button to trigger a new NannyML run. This might be useful after some of the parameters are updated.

<figure><img src="/files/cXPpk7RNnhhrJT1ywdOA" alt=""><figcaption><p>Model settings page.</p></figcaption></figure>

<details>

<summary>General details</summary>

Here, you can change the name of your model.

<img src="/files/fcsWWlxE4P3I6dOY9cfH" alt="" data-size="original">

</details>

<details>

<summary>Datasets</summary>

Under datasets, you can manually add more analysis and target data.

<img src="/files/7ckZ1ZPh4AP1TmGEI0Po" alt="" data-size="original">

</details>

<details>

<summary>Schedule</summary>

Under schedule, you can define when to run the drift and metric calculators.

<img src="/files/mefm9EL49xpj4nrQBceS" alt="" data-size="original">&#x20;

</details>

<details>

<summary>Chunking</summary>

Here, you can choose how to group the results by time interval or size. For example, choosing "monthly" groups all predictions made in the same month and calculates the results. <br>

:bulb: We currently only support **time-based** and **size-based** chunking; if you need support for  number-based chunking, [contact us](https://www.nannyml.com/contact-us).

<img src="/files/nvnxqw2Ce85myAiW1WSl" alt="" data-size="original">

</details>

<details>

<summary>Performance</summary>

Here, you can select the metrics you want to monitor. There is also the option to configure them further. 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.

<img src="/files/mXwZ9Gl0ubAsgH6KfiOQ" alt="" data-size="original">

Under every metric configuration, it is possible to specify further if this metric has to be calculated and/or estimated. NannyML automatically extracts thresholds based on the supplied reference data, but it is possible to configure a custom threshold here. All metrics follow this type of configuration except business value.

<img src="/files/VbFFgMUxQOpjMZH0xSwM" alt="" data-size="original">

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

![](/files/uhD0tdnGBOSGoNZJEMqN)

For business value estimation or calculation, a cost/benefit matrix has to be supplied. This matrix contains the value a single observation in each of the cells of the respective confusion matrix cells brings in or costs. For example, a true positive prediction brings in X amount, and a false positive prediction will cost us Y.

![](/files/YzeTSxPa5BsPFxHuWD7z)

</details>

<details>

<summary>Concept shift</summary>

Here, you can specify which concept shift results to run and configure the threshold values.

<img src="/files/yB6GEJx47AYuIkt5slVj" alt="" data-size="original">

</details>

<details>

<summary>Covariate shift</summary>

In covariate shift settings, you can specify which drift methods to run and also configure the threshold values.

<img src="/files/Wbk294S3N52usfFNL50O" alt="" data-size="original">

Some methods work for categorical and continuous columns; in that case, it can be selected which of those they must run. Also, the threshold can be manually configured.&#x20;

<img src="/files/9JVc3CMSr9vuYBNaoOxN" alt="" data-size="original">

</details>

<details>

<summary>Data quality</summary>

Here, you can select the type of data quality checks and their threshold values.

<img src="/files/lRMGEfuNShCYENmUJzdQ" alt="" data-size="original">

Both missing values and unseen values can be normalized along with default thresholds.

<img src="/files/u7G22bHy4F7bcJElcb3g" alt="" data-size="original">

</details>