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Communications Mining is now part of UiPath IXP. Check the Introduction in the Overview Guide for more details.
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Communications Mining user guide

Last updated Aug 1, 2025

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.

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:



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.

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.

  • The required training amount

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