---
sourceDocument: Xanadu Enable AI
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/xanadu/intelligent-experiences

 Release :

    - xanadu

ft:locale :

    - en-US

ft:publication_title :

    - Xanadu Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# NLU model settings

# NLU model settings {#ariaid-title1}

* Release version: Xanadu
* 
* Updated August 1, 2024
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read

Change your NLU model's name, description, or confidence
threshold on the Settings page of the model overview.

Access the model's settings by navigating to AllNLU WorkbenchModels. Select the tab for your model's application, then your model's name.
On the model's overview, select the Model settings tab.

## Model settings {#nlu-model-settings__section_vyf_d5c_dwb}

In the upper section of the model settings page, you can change the model's name, short description, and business area. You cannot change the model's language, purpose, or scope. To make a model with a different language,
purpose, or scope, see [Creating models](https://servicenow-prod.fluidtopics.net/EN~zZkL5GYkqAEESELkXtQ "Creating models is the first step to taking advantage of Natural Language Understanding (NLU) in your instances. Create models for Virtual Agent and AI Search in the NLU Workbench.").

By default, the Ignore punctuation check box is active. Ignoring
punctuation makes it so that there is less variance between predicted intents and confidence
scores for utterances with slightly different punctuation. For best results, keep the check box
active.

## Model threshold settings {#nlu-model-settings__section_ulz_j5c_dwb}

Here you can adjust how the confidence threshold works in your model.

A threshold is a confidence score represented by a percentage. The confidence threshold of a model determines what intents from that model will be predicted for a given utterance. For example, if the model threshold is 65%, then
an intent will be predicted for an utterance only when the intent has a confidence score that is at least 65%. Setting a threshold that is too low may increase the false positives by predicting intents that should not be a match
for an utterance. On the other hand, a model threshold that is too high may filter out intents that you do want to get predicted. Finding the ideal threshold improves your model's ability to predict intents correctly.  
There are two types of model threshold settings:

* Automatic - Allow the system to choose the optimal confidence threshold for your model. The value is updated dynamically based on test results. This happens in the Test and publish your model phase, where your model's default test set is used.
* Manual - You can manually set the confidence threshold. The system may also recommend a better threshold for the model during testing. You can choose to accept recommendations.
{#nlu-model-settings__ul_bdn_2yy_dwb}

Prebuilt models come with a tuned threshold. The confidence threshold on prebuilt models was chosen specifically for that model.  
Test results include a model threshold recommendation only if they meet the following requirements:

* The test set has a Test Coverage score of at least 60%, with at least 5 test utterances per intent. For more information, see [Test set creation and management](https://servicenow-prod.fluidtopics.net/y3xFPXipXEZ~RyjkoUWSRg "Use the default test set of your NLU model to test the model's performance and accuracy. Manage your test set over time by building or updating its content in the NLU Workbench.").
* The test set has at least 100 utterances.
* The model is not a prebuilt model.
* The recommended threshold would have better results than the current threshold.
{#nlu-model-settings__ul_ygl_1zy_dwb}

Test results with a recommended threshold contain a second graphic. The second graphic shows the prediction percentages with the recommended thresholds applied.

Applying the threshold recommendation may improve the prediction percentages of your model. Select Apply recommendations to change the threshold. The system automatically retrains the model, and the test
results show the prediction percentages with the new threshold.

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