# t-SNE dimensionality reduction

> t-SNE dimensionality reduction - Review examples for taking a DataRobot project and exporting its
> model insights as both machine readable files and plots in various file formats.

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 `2026-05-06T18:17:09.583164+00:00` (UTC).

## Primary page

- [t-SNE dimensionality reduction](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/dim-reduction.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 evaluation and metrics](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/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/Dimensionality%20reduction%20in%20DataRobot%20with%20t-SNE/Dimensionality%20reduction%20in%20DataRobot%20with%20t-SNE.ipynb)

This accelerator provides examples for taking a DataRobot project and exporting its model insights as both machine readable files and plots in various file formats using t-Distributed Stochastic Neighbor Embedding (t-SNE). t-SNE is a powerful technique for dimensionality reduction that can effectively visualize high-dimensional data in a lower-dimensional space. Dimensionality reduction can improve machine learning results by reducing computational complexity of the algorithms, preventing overfitting, and focusing on the most relevant features in the dataset. Note that this technique should only be used when the number of features is low.