# Symbolic regression (Eureqa)

> Symbolic regression (Eureqa) - Apply symbolic regression to your dataset in the form of the Eureqa
> algorithm.

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 `2026-05-06T18:17:09.582174+00:00` (UTC).

## Primary page

- [Symbolic regression (Eureqa)](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/tune-eureqa.html): Full documentation for this topic (HTML).

## 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.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Model building and fine-tuning](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/fine_tuning_with_eureqa/fine_tuning_with_eureqa.ipynb)

DataRobot offers the ability to apply symbolic regression to your dataset in the form of the Eureqa algorithm. Eureqa returns human-readable and interpretable analytic expressions and allows us to incorporate DataRobot's own domain expertise about the problem.

This accelerator shows how the Eureqa algorithm can "discover" the gravitational constant by finding the correct relationship between the variables from a double-pendulum experiment.

This accelerator covers the following activities:

- Apply the Eureqa algorithm to your dataset
- Tune the model's mathematical building blocks to incorporate DataRobot's domain expertise about the problem
- Access the resulting closed-form expression