

An Active Learning project consists of a few core components:
You must create each of these components before you can create an Active Learning project. Even if these items already exist in the workspace, you may want to create a new instance of each specifically for your new project.
The first component you must create for your Active Learning project is a saved search of all documents to be used in the Active Learning project. This set of documents is used as the data source when you create an Analytics classification index.
A few considerations for the search:
Note: If relevant documents are very rare, then targeted searches for relevant documents outside of the queue can help make Active Learning more effective.
There are a few steps you can take when creating your saved search to optimize your Active Learning project.
To streamline the start of your project, we recommend taking steps to reduce document clutter in your data source. This can include any combination of the following:
The Active Learning model only builds once you have at least five documents coded with the positive choice and five coded with the negative choice. We recommend having reviewers code a sample of documents prior to starting your Active Learning project to help start the model's learning and begin the project with a model build and a ranking of all documents. This can take place before or after you create the project.
Note: In Prioritized Review, if there are fewer than five coded documents for either choice, the system serves random documents until the threshold number of documents are coded. These documents appear under the Index Health column in Review Statistics.
There are a few ways to identify documents to pre-code, and you will usually combine more than one strategy:
Note: Aim for an even distribution of documents pre-coded on the positive/responsive designation as on the negative/not responsive designation when possible. For example, If your richness is low and you expect to find not responsive documents easily, then focus your effort on finding more responsive documents to pre-code.
We recommend taking a richness sample to pre-code documents. Richness refers to the percentage of documents in a project that are responsive. We recommend calculating a richness estimate at the start of the project to give you a metric to gauge the progress of your project against.
To calculate richness, you can run an early Project Validation round which has Elusion with Recall selected. See Project Validation and Elusion Testing for more information.
Alternately, you can use the following method:
First, use the Sampling tool to take a sample of documents from your data source and have reviewers code all of those documents on the project review field:
To calculate the richness estimate, divide the number of documents the reviewers found to be relevant by the total number of documents in the sample.
For example, imagine you ran a sample of 500 documents and your reviewers found 80 to be relevant. The richness for this sample would be 80 / 500 = .16, or 16%.
To estimate the number of responsive documents in the entire project, multiply the richness from the sample by the total number of documents in the saved search.
For example, if the total number of documents in the saved search used to create your Active Learning is 100,000, the estimate of the number of relevant documents for the entire project would be .16 x 100,000 = 16,000. This mean there will likely be around 16,000 relevant documents in your project. This prediction could be a little high or a little low, depending on the margin of error.
You can use this estimate to monitor the progress of your Active Learning project. For example, after you start your Active Learning project you see that your reviewers have coded 8,000 documents as relevant. Using the estimate, you can assume that you have found about half of all relevant documents in your case.
Active Learning uses an Analytics classification index as the basis of its machine learning. The classification index uses Support Vector Machine learning (SVM) to predict the relevance of uncoded documents in Active Learning based on their closeness to coded examples. To learn more about Support Vector Machine learning (SVM), see Analytics indexes.
Once you have the set of documents you want to use for Active Learning in a saved search, you can create the classification index. To create the classification index:
You can continue creating the other required components for Active Learning while you wait for the index to activate. However, the index must be active in order to create the Active Learning project.
When you create an Active Learning project, you designate a reviewer group which is the set of users you want to code documents in the Active Learning project. You can create a new group specifically for your Active Learning project, or you can use an existing group. Ensure the group is added to the workspace where you're running Active Learning.
The reviewer group accessing the Active Learning project must have the following permissions:
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Note: The users in this group are not automatically added to the Active Learning project. You must grant each individual access to the Review queue.
Other considerations for setting group permissions:
Active Learning requires a single choice field with at least two choices for reviewers to code on. One choice must represent the positive/responsive designation, and another must represent the negative/not responsive designation. Any additional choices will be considered neutral, and they will not be used to train the Active Learning model.
Reviewers may also code documents on other fields that you include in your review layout; however, only this review field is used by the Active Learning model.
Note: We recommend turning off family propagation with Active Learning.
Note: You can edit these choices after you create the project.
Active Learning does not create a layout automatically; you must create or modify an existing layout for reviewers to make coding decisions using the review field created above. This layout is not a prerequisite for creating your Active Learning project, but the layout should exist before the reviewer group begins coding in Active Learning. You can also include other fields on this layout. Active Learning only learns from the review field.
Once you've configured the required workspace components, you can create your Active Learning project
To create an Active Learning project, complete the following:
You are then redirected to the project home dashboard.
Note: Analytics classification indexes are copied over when a workspace is used as a template, as are conceptual indexes. However, you can't copy an Active Learning project in a template.
The Active Learning Project layout contains the following fields:
Note: We recommend setting the Suppress Duplicate Documents setting to No for Prioritized Review and Yes for Coverage Review. Note that you cannot change this setting once you create your project.
Note: Once a project is created, you cannot edit the fields. Any additional choices created for the review field will be considered neutral.
Upon saving a new project, Relativity creates a few different objects specific to Active Learning.
This view is automatically secured to the reviewer group. Initially the view will be empty, but as reviewers code documents in Active Learning queues, the view returns documents previously reviewed by the currently logged in reviewers. This is the document list view that's tied to the Active Learning project. It has the same name as the Active Learning project and is customizable by admins. This is the only place a reviewer can enter a project queue.
Relativity also creates new fields that can be used for custom document list reporting. These fields are updated per document as reviewers code on the project review field.
For more information on using these fields in reports and dashboards, see Monitoring an Active Learning project.
If you chose to suppress duplicate documents, you can view those documents from the Field Tree. You can find the tag for suppressed duplicate documents under the Classification index associated with the Active Learning project. Documents are tagged with the <Index Name> - Suppressed Duplicate tag as they're identified during review.
There is currently no way to add these documents back into the review queue or to identify which coded document caused a document to be suppressed. However, you can code these documents manually and they are indicated as manually-selected documents in the project.
Once you've created an Active Learning project, you're redirected to the Project Home tab. In the top-right corner of the Project Home tab are three icons.
Note: Click the icon to edit the <Positive Choice> Cutoff value. This rank divides positive and negative predictions, such as "relevant" and "not relevant." 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.
Include Index Health in Prioritized Review
Note: Click the icon to change whether index health documents are served up for review. This setting only affects Prioritized Review queues. For more information, see Turning off index health documents.
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