Coverage Coverage is a term frequently used in Machine Learning and relates to how well a model 'covers' the data it's used to analyse. In Communications Mining, this relates to the proportion of messages in the dataset that have informative label predictions, and is presented in Validation as a percentage score. '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, e.g. 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. The visual below gives an indication of what low coverage versus high coverage would look like across an entire dataset. Imagine the shaded circles are messages that have informative label predictions.