# Partial dependence plots (PDP/ICE)

> Partial dependence plots (PDP/ICE) - Create one-way and two-way partial dependence plots (PDP), and
> Individual Conditional Expectations (ICE) insights using DataRobot.

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 `2026-05-06T18:17:09.583802+00:00` (UTC).

## Primary page

- [Partial dependence plots (PDP/ICE)](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/pdp-ice.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/PDP_ICE/PDP%20and%20ICE%20AV.ipynb)

This accelerator presents an example workflow to create one-way and two-way partial dependence plots (PDP), and Individual Conditional Expectations (ICE) insights using DataRobot.

This accelerator has two parts:

1. Score data against a deployment and join the predictions back with the full dataset.
2. Use the scored dataset to gain insights by generation PDP and ICE plots.