# Cross-Class Accuracy

> Cross-Class Accuracy - How to use the Cross-Class Accuracy table to understand the model's accuracy
> performance for each protected class.

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 `2026-04-24T16:03:56.578892+00:00` (UTC).

## Primary page

- [Cross-Class Accuracy](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/cross-acc.html): Full documentation for this topic (HTML).

## Sections on this page

- [Cross-Class Accuracy table](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/cross-acc.html#cross-class-accuracy-table): In-page section heading.

## Related documentation

- [Classic UI documentation](https://docs.datarobot.com/en/docs/classic-ui/index.html): Linked from this page.
- [Modeling](https://docs.datarobot.com/en/docs/classic-ui/modeling/index.html): Linked from this page.
- [Model insights](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/index.html): Linked from this page.
- [Bias and Fairness](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/index.html): Linked from this page.
- [Optimization metric](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html): Linked from this page.
- [F1](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/metrics-classic.html): Linked from this page.
- [AUC (Area under the curve)](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/roc-curve-classic.html#area-under-the-roc-curve): Linked from this page.

## Documentation content

# Cross-Class Accuracy

The Cross-Class Accuracy tab calculates, for each protected feature, evaluation metrics and ROC curve-related scores segmented by class. Use these metrics to better understand how well the model is performing, and its behavior on a given protected feature/class segment.

## Cross-Class Accuracy table

Use the Cross-Class Accuracy table to understand the model's accuracy performance for each protected class. Change the protected feature using the dropdown at the top.

The table below describes each accuracy metric:

| Metric | Description |
| --- | --- |
| Optimization metric (LogLoss in this example) | Displays the optimization metric selected on the Data page before model building. |
| F1 | Reports the model's accuracy score, computed based on precision and recall. |
| AUC (Area under the curve) | Measures how well the model can distinguish between classes. |
| Accuracy | Measures the percentage of correctly classified instances. |

The above example compares LogLoss (the project's optimization metric) between male and female. The score for females is lower, meaning the model is better at predicting salary rate correctly for females than males.