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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

How validation works

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

Within Validation, the platform evaluates the performance of both the label and general field models associated with a dataset.

For the label model specifically, it calculates an overall Model Rating by testing a number of different performance factors, including:

  • How well it is able to predict each label in the taxonomy, using a sub-set of training data from within that dataset.
  • How well covered the dataset as a whole is by informative label predictions.
  • How balanced the training data is, in terms of how it has been assigned and how well it represents the dataset as a whole.

Assessing label performance

To assess how well it can predict each label, the platform first splits the reviewed, that is, annotated messages in the dataset into the following groups:
  • a majority set of training data.
  • a minority set of test data.

In the following image, the coloured dots represent the annotated messages within a dataset. This split is determined by the message ID when the messages are added to the dataset, and remains consistent throughout the life of the dataset.



The platform then trains itself using only the training set as training data.

Based on this training, it then tries to predict which labels should apply to the messages in the test set and evaluates the results for both precision and recall against the actual labels that were applied by a human user.

On top of this process, the platform also takes into account how labels were assigned, that is, which training modes were used when applying labels to understand whether they have been annotated in a biased, or balanced way.

Validation then publishes live statistics on the performance of the labels for the latest model version, but you can also view historic performance statistics for previously pinned model versions.

Assessing coverage

To understand how well your model covers your data, the platform looks at all of the unreviewed data in the dataset and the predictions that the platform has made for each of those unreviewed messages.

It then assesses the proportion of total messages that have at least one informative label predicted.

Informative labels' are those labels that the platform understands to be useful as standalone labels, by looking at how frequently they're assigned with other labels. Labels that are always assigned with another label. For example, parent labels that are never assigned on their own or Urgent if it's always assigned with another label, are down-weighted when the score is calculated.

Assessing balance

When the platform assesses how balanced your model is, it's essentially looking for annotating bias that can cause an imbalance between the training data and the dataset as a whole.

To do this, it uses a annotating bias model that compares the reviewed and unreviewed data to ensure that the annotated data is representative of the whole dataset. If the data is not representative, model performance measures can be misleading and potentially unreliable.

Annotating bias is typically the result of an imbalance of the training modes used to assign labels, particularly if too much 'text search' is used and not enough 'Shuffle'.

The Rebalance training mode shows messages that are under-represented in the reviewed set. Annotating examples in this mode will help to quickly address any imbalances in the dataset.

When validation occurs

Every time you complete some training within a dataset, the model updates and provides new predictions across every message. In parallel, it also re-evaluates the performance of the model. This means that by the time the new predictions are ready, new validation statistics should also be available (though one process can take longer than the other sometimes), including the latest .

Note: The platform will always show you as default the latest validation statistics which have been calculated, and will tell you if new statistics are yet to finish being calculated.

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