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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

True and false positive and negative predictions

It is important to understand these definitions, as they form a key part of explaining other fundamental machine learning (ML) concepts such as precision and recall.

The following definitions are outlined in the context of their application within the platform:

  • A positive prediction is one where the model thinks that a label applies to a message.
  • A negative prediction is one where the model thinks that a label does not apply to a message.

True positives - A true positive result is one where the model correctly predicts that a label applies to a message.

True negatives - A true negative result is one where the model correctly predicts that a label does not apply to a message.

False positives - A false positive result is one where the model incorrectly predicts that a label applies to a message, when in fact it does not apply.

False negatives - A false negative result is one where the model incorrectly predicts that a label does not apply to a message, when in fact it does apply.

To understand each of these concepts in more detail, check Precision and recall explained.

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