> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/developer/rest-api/model-evaluations/confusion-matrix.md).

# Confusion Matrix

Returns the aggregated confusion matrix derived from per-image predictions. Each cell `matrix[actual][predicted]` is the count of instances where the ground-truth class was `actual` and the model predicted `predicted`. For semantic segmentation evaluations, values represent pixel counts rather than instance counts.

This is the data the **confusion matrix** panel in the app reads.

```url
https://api.roboflow.com/:workspace/model-evals/:evalId/confusion-matrix
```

```bash
curl "https://api.roboflow.com/my-workspace/model-evals/$EVAL_ID/confusion-matrix?api_key=$ROBOFLOW_API_KEY&split=test"
```

## Query parameters

| Parameter    | Type    | Description                                                                                     |
| ------------ | ------- | ----------------------------------------------------------------------------------------------- |
| `split`      | enum    | One of `train`, `valid`, `test`, or `all`. Default `test`.                                      |
| `confidence` | integer | Confidence-threshold percentage in `[0, 100]`. Defaults to the canonical file (typically `20`). |

## Response

```json
{
    "split": "test",
    "confidenceThreshold": 0.2,
    "classes": ["Car-rims", "music-note", "background"],
    "matrix": [
        [20,  0, 0],
        [ 0,  0, 0],
        [80,  0, 0]
    ]
}
```

In the example above, at confidence threshold 0.2:

* All 20 instances of `Car-rims` were correctly classified (`matrix[0][0] = 20`)
* The model produced 80 false positives - predicting `Car-rims` when the actual class was `background` (`matrix[2][0] = 80`)
* The test split has no `music-note` instances

## Notes

* `confidence` selects which underlying per-confidence variant of the report to aggregate. Different thresholds yield different matrices.
* `split=all` aggregates raw counts across train, valid, and test.


---

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