# Training using Low confidence

> :::note
You must have assigned the **Source - Read** and **Dataset - Review** permissions as an Automation Cloud user, or the **View sources** and **Review and annotate** permissions as a legacy user.
:::

:::note
You must have assigned the **Source - Read** and **Dataset - Review** permissions as an Automation Cloud user, or the **View sources** and **Review and annotate** permissions as a legacy user.
:::

The final key step in **Explore** is training using the **Low confidence** mode, which shows you messages that are not well covered by informative label predictions. These messages will have either no predictions or very low confidence predictions for labels that the platform understands to be informative.

**Informative labels** are those labels that the platform understands to be useful as standalone labels, by looking at how frequently they are assigned with other labels.

This is a very important step for improving the overall coverage of your model. If you see messages which should have existing labels predicted for them, this is a sign that you need to complete more training for those labels. If you identify relevant messages for which no current label is applicable, you may want to create new labels to capture them.

To access the **Low confidence** mode, use the dropdown menu from the **Explore** page, as shown in the following image:

![This image depicts the dropdown menu to access the Low confidence label.](https://dev-assets.cms.uipath.com/assets/images/ixp/ixp-this-image-depicts-the-dropdown-menu-to-access-the-low-confidence-label-583296-dd59625e.webp)

## The required training amount

The **Low confidence** mode will present you with 20 messages at a time, and you should complete a reasonable amount of training in this mode, going through multiple pages of messages and applying the correct labels, to help increase the coverage of the model. For a detailed explanation of coverage, check [When to stop training your model](https://docs.uipath.com/ixp/automation-cloud/latest/cm-user-guide/when-to-stop-training-your-model).

The total amount of training you need to complete in **Low confidence** depends on a few different factors:

* How much training you completed in **Shuffle** and **Teach**. The more training you do in **Shuffle** and **Teach**, the more your training set should be a representative sample of the dataset as a whole, and the fewer relevant messages there should be in **Low confidence**.
* The purpose of the dataset. If the dataset is intended to be used for automation and requires very high coverage, then you should complete a larger proportion of training in **Low confidence** to identify the various edge cases for each label.

At a minimum, you should aim to annotate five pages of messages in this mode. Later on in the **Refine** phase when you come to check your coverage, you may find that you need to complete more training in **Low confidence** to improve your coverage further.