Adding missed labels helps improve the label precision and recall in UiPath Communications Mining. Precision is the percentage of correctly labeled verbatims out of all the verbatims that have the label applied, while recall is the percentage of correctly labeled verbatims out of all the verbatims that should have the label applied. By adding missed labels, you are increasing the recall of the label, as you are reducing the number of false negatives (verbatims that should have the label but do not). This also improves the precision of the label, as you are reducing the noise in the data and making the label more informative and consistent. Adding missed labels is one of the recommended actions that the platform suggests to improve the model rating and performance of the labels.

References: Communications Mining - Training using ‘Check label’ and ‘Missed label’, Communications Mining - Model Rating