You can look at the Document Rank Distribution chart to visualize how this will categorize your documents. This value determines the minimum rank needed for a document to be predicted as responsive. In order to update ranks, you must specify the Cutoff. Since ranks change, at least slightly, with each model build, a project administrator can choose to update these ranks on demand. Because it is a SQL-intensive operation, ranks are only stored on the document object on demand. For instance, you could batch out to reviewers all documents above a certain rank. These document ranks drive the Prioritized Review and Coverage Review queue logic, but they can also be imported into the Document object for other workflows. Note: When you update the Cutoff value, the value is updated in all three places where it’s used in the application: Project Validation, Update Ranks, and Project Settings.Īctive Learning ranks all documents from 100 (most likely to be in the positive category) to 0 (most likely to be in the negative category). Note that updating ranks doesn't change the coding decision on the document. You can use Update Ranks to manually update the document ranks and ensure the rank categorization field is up to date. In the top-right corner of the Project Home, you'll see several icons. Note: If a coding decision is updated on a document reviewed in the queue, it will not change to a manually selected document. Skipped - the number of documents served to a reviewer that weren't coded by an end reviewer.These documents are not used to teach the model. This count includes documents coded in the review queue and manually-selected documents. Coded Neutral - the number of documents that were coded with one of the neutral choices on the review field.Negative Choice manually-selected - the number of documents that were coded on the negative choice designation field outside of the review queue.These documents are used to teach the model. Coded - the number of documents coded with the negative designation in the review field.Positive Choice manually-selected - the number of documents that were coded on the positive choice designation field outside of the review queue.Coded - the number of documents coded with the positive designation on the review field.Documents that were removed during the index build are excluded from this count. This count reflects the documents successfully indexed. Project Size - the number of documents in the project.The project home dashboard contains the following statistics: How many documents have been coded neutral or skipped.How many documents have been coded outside the queue (manually-selected documents).How many documents have been coded in your project.Use the Project Home dashboard to understand the following: After you first create the project, the dashboard displays the Project Size and coding statistics based on the pre-coded documents in your data source, if they exist. The Project Home dashboard gives a high-level overview of the documents in your Active Learning project. This page contains the following information: Note: Each time an admin accesses the Project Home page - via a page refresh or from a different page - the latest data will reflect in the Project Home display. There are a number of ways to monitor the progress of an Active Learning project using tools on the Project Home and Review Statistics tabs. This rebuild incorporates all coding decisions made after the last build began, and it includes any coding decisions made outside of the queue. After that build finishes, a project being actively coded in the queue will rebuild the model every 20 minutes after the previous build. The Active Learning model starts its first build after at least five documents have been coded with the positive choice and five have been coded with the negative choice. The actual Active Learning process takes place using a combination of time and detection of new coding decisions.
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