# Rating table

> Rating table - Learn how to download and upload rating tables for Generalized Additive Models.

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 `2026-04-24T16:03:56.285727+00:00` (UTC).

## Primary page

- [Rating table](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/rating_table.html): Full documentation for this topic (HTML).

## Sections on this page

- [Download a rating table](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/rating_table.html#download-a-rating-table): In-page section heading.
- [Upload a rating table](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/rating_table.html#uploading-a-rating-table): In-page section heading.

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [Python API client user guide](https://docs.datarobot.com/en/docs/api/dev-learning/python/index.html): Linked from this page.
- [Modeling](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/index.html): Linked from this page.
- [Model insights](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/index.html): Linked from this page.

## Documentation content

# Rating table

A rating table is an exportable csv representation of a Generalized Additive Model.
They contain information about the features and coefficients used to make predictions.
Users can influence predictions by downloading and editing values in a rating table, then re-uploading the table and using it to create a new model.

See the page about interpreting Generalized Additive Models’ output in the DataRobot user guide for more details on how to interpret and edit rating tables.

## Download a rating table

You can retrieve a rating table from the list of rating tables in a project:

```
import datarobot as dr
project_id = '5506fcd38bd88f5953219da0'
project = dr.Project.get(project_id)
rating_tables = project.get_rating_tables()
rating_table = rating_tables[0]
```

Or you can retrieve a rating table from a specific model.
The model must already exist:

```
import datarobot as dr
from datarobot.models import RatingTableModel, RatingTable
project_id = '5506fcd38bd88f5953219da0'
project = dr.Project.get(project_id)

# Get model from list of models with a rating table
rating_table_models = project.get_rating_table_models()
rating_table_model = rating_table_models[0]

# Or retrieve model by id. The model must have a rating table.
model_id = '5506fcd98bd88f1641a720a3'
rating_table_model = dr.RatingTableModel.get(project=project_id, model_id=model_id)

# Then retrieve the rating table from the model
rating_table_id = rating_table_model.rating_table_id
rating_table = dr.RatingTable.get(projcet_id, rating_table_id)
```

Then you can download the contents of the rating table:

```
rating_table.download('./my_rating_table.csv')
```

## Upload a rating table

After you’ve retrieved the rating table CSV and made the necessary edits, you can re-upload the CSV so you can create a new model from it:

```
job = dr.RatingTable.create(project_id, model_id, './my_rating_table.csv')
new_rating_table = job.get_result_when_complete()
job = new_rating_table.create_model()
model = job.get_result_when_complete()
```