---
title: "About the Sysrev's prediction model"
url: "https://help.sysrev.com/en/article/about-sysrevs-prediction-model"
language: "en"
last_updated: "2025-12-17T14:53:18.946Z"
---

# About the Sysrev's prediction model

Sysrev will make predictions about the likely label answer for all Boolean and categorical labels (including the default include/exclude label) based on existing label answers provided by human reviewers in a project. In this way, Sysrev supports prioritized screening such that reviewers can review records based on their likelihood of inclusion or exclusion in a review, speeding up the review process and allowing for bulk exclusion of records should review teams choose to take that approach. 

Sysrev uses a [Stochastic Gradient Descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) model for prediction. Stochastic Gradient Descent (SGD) is an iterative optimization algorithm that updates model parameters using the gradient from a single randomly selected data point or small batch.This model can be used for multi-label classification (such as categorical labels) and is fast and scalable. 

## **How and when the model runs**

Sysrev will automatically run the prediction model at least **once every hour**, in general, when sufficient reviewer labels have been added to a project. Predictions are not generated if there's not enough class coverage (i.e., if you only mark labels true or you don't use every categorical value), however, as soon as there is *at least one* reviewer answer for all possible label answers in a given label, the model will generate predictions. That said, the model need sufficient data before it can make *good* predictions. The more human reviewer data it has, the better the results.

For categorical labels, the prediction model will treat each category as a yes/no Boolean label. In other words, for each category option in a categorical label, it will predict how likely an article is to be labeled with a given category.