Test your model
Test your Natural Language Understanding (NLU) model against its default test set. Testing helps determine how your model is performing with the current content.
Before you begin
- Make sure that the NLU Model Builder - Core plugin, NLU Model Builder plugin, NLU Workbench - Advanced Features plugin and Predictive Intelligence plugin are all installed and activated.
- Have a trained model for Virtual Agent or AI Search. For more information, see Build and train your model.
- Have a test set for testing models. For more information, see Test set creation and management.
- Role required: nlu_editor, nlu_admin, or admin. The editor must be assigned to the model.
About this task
- If an expected intent in your test set does not correspond to any intent in the model, the utterances with those intents are not used for testing. They are not included in the test results.
- The mid-conversation responses of Dialog Acts can't be tried or tested in NLU Workbench.
- When the model returns no prediction for utterances which are marked as Not relevant, that result will be counted as Correct.
- If your test set does not cover at least 60% of intents in the model, the system will not recommend a confidence threshold. However, you can still run the test.
In this example scenario, you've trained your model and want to assess the performance.
Procedure
Result
When the test is finished, the Test and publish your model page reloads. The Test run date field reflects the date and time of this test.
The Overview tab displays a chart of the test results. It also displays a list of the top 5 incorrect intents and the top 5 missed intents.
The Detailed results tab lists all of the test utterances and their prediction outcomes.
You can see previous test results by clicking view the test history on Test and publish your model, or by navigating to .
What to do next
Use the results to edit and improve your model's content. When you are satisfied with the results, publish your model to make it available to consuming applications such as Virtual Agent.