# Rating Tables

> Rating Tables - How to display a model’s Rating Table tab, and export the model's validated
> parameters. Validation ensures correct parameters and reproducible results.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-04-24T16:03:56.582596+00:00` (UTC).

## Primary page

- [Rating Tables](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/rating-table-classic.html): Full documentation for this topic (HTML).

## Sections on this page

- [Download rating tables](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/rating-table-classic.html#download-rating-tables): In-page section heading.
- [Modify rating tables](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/rating-table-classic.html#modify-rating-tables): In-page section heading.
- [Workflow overview](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/rating-table-classic.html#workflow-overview): In-page section heading.
- [Detailed workflow](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/rating-table-classic.html#detailed-workflow): In-page section heading.
- [Rating table validation](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/rating-table-classic.html#rating-table-validation): In-page section heading.

## Related documentation

- [Classic UI documentation](https://docs.datarobot.com/en/docs/classic-ui/index.html): Linked from this page.
- [Modeling](https://docs.datarobot.com/en/docs/classic-ui/modeling/index.html): Linked from this page.
- [Model insights](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/index.html): Linked from this page.
- [Describe](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/index.html): Linked from this page.
- [specify the pairwise interactions](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ga2m.html): Linked from this page.
- [the considerations](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/rating-tables.html#feature-considerations): Linked from this page.
- [coefficients](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/coefficients-classic.html): Linked from this page.
- [Worker Queue](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/worker-queue.html): Linked from this page.

## Documentation content

# Rating Tables

When a  model displays the rating table [https://docs.datarobot.com/en/docs/images/icon-rating.png](https://docs.datarobot.com/en/docs/images/icon-rating.png) icon on the Leaderboard, you can export the model's complete, [validated](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/rating-table-classic.html#rating-table-validation) parameters. Validation assures that the downloaded parameters are correct and that you can reproduce the model's performance outside of DataRobot. For organizations that have the capability enabled, you can modify the table coefficients and [apply the new table to the original (parent) model](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/rating-table-classic.html#modify-rating-tables), resulting in a new "child" model available on the Leaderboard.

Note that, for GA2M models, you can [specify the pairwise interactions](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ga2m.html) included in the model's output.
Before working with rating tables, review [the considerations](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/rating-tables.html#feature-considerations).

## Download rating tables

To export rating table coefficients:

1. From the Leaderboard, identify a model with thisicon, indicating that it produced a rating table.
2. Expand the model and click theRating Tabletab. (The screen may appear different, depending on your permissions.)
3. Click theDownload Tablelink to save the CSV file. See thisadditional informationfor help interpreting the rating table output.
4. Modify your rating table in a text editor or spreadsheet application. If applicable, you can nextupload the modified tableto the parent and create a new child model with the table.

## Modify rating tables

When you modify a rating table and upload it to the original parent model (and then run the model), DataRobot creates a child model with the modified version of the original parent model's rating table. Available from the Leaderboard, the new model has access to the same features as the parent (with these exceptions).

The following, briefly and then [in detail](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/rating-table-classic.html#detailed-workflow), describes the workflow for creating a new child model.

### Workflow overview

The following outlines the steps to iterate on building models with modified rating tables:

1. Download the rating table from the parent.
2. Modify the rating table outside of DataRobot using an appropriate editor .
3. Upload the modified table to the parent model.
4. Score the new model, adding it to the Leaderboard.
5. Click Open Child Model to view the new model.
6. To iterate on rating table changes, download the child's rating table.
7. Modify the child's rating table outside of DataRobot.
8. Upload the newly modified table to the parent model.
9. Return to step 4 and repeat as necessary.

### Detailed workflow

The following describes, in more detail, the steps for working with rating tables:

1. Select a model from the Leaderboard that displays the rating table icon. This is the parent model.
2. Downloadthe parent model's rating table.
3. Edit thecoefficientsin the rating table CSV file using anappropriate editoror spreadsheet.
4. Once you have completed modifications to the exported rating table, drag-and-drop or browse to upload the new rating table: All available (newly and previously uploaded) ratings tables are listed underUploaded Tables.
5. If desired, and only before you run the model, you can click the pencil icon to rename the uploaded table, up to 50 characters. Note that the child model's name is based on the name of the rating table it was created from. You can also rename the table outside of the application. If you specify an existing name, DataRobot appends a numeric to the table name.
6. Click theAdd to Leaderboardlink to create and score the new model. DataRobot first validates the new rating table and, after building completes, the new child model is available on the Leaderboard. A green check indicates a successfully validated and uploaded table; otherwise, DataRobot displays an error message indicating the issue. (You can monitor build status in theWorker Queue.)
7. Once the build completes, click theOpen Child Modellink corresponding to the child model/rating table pair you would like to view. DataRobot opens (and places you in) theRating Tablestab of the child model. The child model name isModified Rating Table: <rating_table_name>.csvand is visible and accessible from the Leaderboard.

From the child model, you can do the following:

| Link | Action |
| --- | --- |
| Download Table | Download the rating table of the child model. To iterate on coefficient changes in a table, download the child's rating table, upload the modified child rating table to the parent, compare scores, and continue the process as necessary. |
| Open Parent Model | Move back to the Rating Tables tab of the parent (original) model. From there you can upload new tables, build new models, or open any built child models. |

> [!NOTE] Note
> You cannot upload a new rating table to the child model. You can only upload rating tables to the parent model.

## Rating table validation

When DataRobot builds a model that produces rating tables (for example, GA2M), it runs validation on the model before making it available from the Leaderboard. For validation, DataRobot compares predictions made by Java rating table Scoring Code (the same predictions to produce that specific Rating Table) against predictions made by a Python code model in the DataRobot application that is independent from the rating table CSV file. If the predictions are different, the rating table fails to validate, and DataRobot marks the model as errored.