> For the complete documentation index, see [llms.txt](https://docs.datasaur.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.datasaur.ai/assisted-labeling/predictive-labeling.md). # Predictive Labeling ## Introduction Predictive labeling uses machine learning to generate label predictions based on a subset of manually labeled data. This feature reduces the time and effort required for manual labeling. Once predictions are generated, they can be accepted or rejected. Predictive labeling is especially useful for large datasets, but predicted results should always be reviewed to prevent errors.

## Key features **Predictive labeling** provides the following benefits: * **Efficiency**: Reduces manual labeling effort by using model predictions. * **Consistency**: Helps maintain consistent labeling across large datasets. * **Scalability**: Supports labeling at scale for large volumes of data. ## Quick start guide Follow these steps to use predictive labeling in a row labeling project: ### Enable Predictive labeling In a row labeling project, click the gear icon in the extension panel on the right to open the **Manage extensions** dialog. Then, enable the **Predictive labeling** extension.

### Configure input and output fields Set the configuration in the extension: * **Input column(s)**: Columns used as context for prediction. * **Target field**: Field where predicted labels will be stored.

### Save configuration and start prediction Click **Save configuration** to start the prediction. * If labeled data already exists, predictions will be generated immediately. * If not, label at least 5 examples per answer option first. For example, if there are two answer options: `POSITIVE` and `NEGATIVE` , label at least 5 examples as `POSITIVE` and 5 examples as `NEGATIVE.` ### Review predictions Review generated predictions and accept or reject them. This iterative process helps improve labeling quality over time.