> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/sdgm/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/sdgm/_mcp/server.

# Baselines

> **Prerelease documentation**
>
> This Kumo Studio information is prerelease and is not yet supported. Stay tuned for the Docker and Kubernetes releases.

## Baseline Evaluation Metrics

On your **training job’s evaluation page**, Kumo compares your model against heuristic baselines for temporal binary classification, link prediction, and regression tasks.
These baselines show how much your model improves over simple heuristic-based approaches.

Kumo computes baselines at the per-entity level:

* **Regression tasks** use past labels as forecasts.
* **Link prediction tasks** recommend past user behavior as future predictions.

> **Warning**
>
> Kumo does not support baselines when `timeframe_step` is `None`.

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## Baselines Per Task Type

The following table describes the baselines Kumo supports, organized by prediction task type:

| Task Type             | Baseline                                                                                                                                                                                                                               |
| --------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Link Prediction       | **Per Entity Frequency** - Predicts each entity using the most popular ranked list of target values from all previous splits.                                                                                                          |
| Regression            | **Per Entity Mean (Personalized Average)** - Predicts each entity using the average of all past target values from previous splits. **Per Entity Last Label** - Predicts each entity using the most recent past value as the forecast. |
| Binary Classification | **Per Entity Mean** - Predicts each entity using the average of all target values from previous splits. **Per Entity Last Label** - Predicts each entity using the previous time frame’s label.                                        |

### Why Use Baselines?

Baselines provide a reference point for measuring how much your Kumo model improves over simple heuristics.
A well-performing model should outperform these baselines significantly.