1. A computer-implemented method of automatically classifying digital content, the method comprising: creating, by an auto-classification system embodied on non-transitory computer memory, a classification model for classifying digital content, wherein creating the classification model comprises: prompting a user via a user interface to enter a name and description for the classification model, and responsive to user selection of one or more documents via the user interface, adding or importing, from a document source into a container, the selected one or more documents as exemplars for a category of digital content within the classification model; subsequent to a user selecting a plurality of documents, the auto-classification system classifying the plurality of documents using characteristics of the exemplars; during the classifying, after the classifying is complete, or both during the classifying and after the classifying is complete, the auto-classification system determining performance metrics representing accuracy of the classification model in classifying the plurality of documents, the performance metrics including precision, recall, match, noise, and silence metrics; the auto-classification system determining one or more recommended actions based on the performance metrics; the auto-classification system displaying the performance metrics including the precision, recall, match, noise, and silence metrics for a number of the plurality of documents that have been classified using the classification model; the auto-classification system displaying a user feedback guide that presents the one or more recommended actions to improve the accuracy of the classification model, wherein the one or more recommended actions comprise adding one or more exemplars from the number of the plurality of documents that have been classified using the classification model or specifying how to meet one or more associated content classification rules; and providing a user interface element associated with the user feedback guide that, in response to receipt of user input, causes a recommended action to be performed on the classification model.
1. A computer-implemented method of automatically classifying digital content, the method comprising: creating, by an auto-classification system embodied on non-transitory computer memory, a classification model for classifying digital content, wherein creating the classification model comprises: prompting a user via a user interface to enter a name and description for the classification model, and responsive to user selection of one or more documents via the user interface, adding or importing, from a document source into a container, the selected one or more documents as exemplars for a category of digital content within the classification model; subsequent to a user selecting a plurality of documents, the auto-classification system classifying the plurality of documents using characteristics of the exemplars; during the classifying, after the classifying is complete, or both during the classifying and after the classifying is complete, the auto-classification system determining performance metrics representing accuracy of the classification model in classifying the plurality of documents, the performance metrics including precision, recall, match, noise, and silence metrics; the auto-classification system determining one or more recommended actions based on the performance metrics; the auto-classification system displaying the performance metrics including the precision, recall, match, noise, and silence metrics for a number of the plurality of documents that have been classified using the classification model; the auto-classification system displaying a user feedback guide that presents the one or more recommended actions to improve the accuracy of the classification model, wherein the one or more recommended actions comprise adding one or more exemplars from the number of the plurality of documents that have been classified using the classification model or specifying how to meet one or more associated content classification rules; and providing a user interface element associated with the user feedback guide that, in response to receipt of user input, causes a recommended action to be performed on the classification model. 4. The method as claimed in claim 1 wherein displaying the performance metrics comprises displaying values for precision and recall, wherein the precision represents a frequency with which an assigned classification matches an expected classification and wherein the recall represents a frequency with which the expected classification is assigned across all processed documents.