# Confusionmatrix

> Compute the ConfusionMatrix metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute ConfusionMatrix, or asks how to score with ConfusionMatrix.

- Skill: `qhjqhj00/confusionmatrix` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/confusionmatrix`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/confusionmatrix/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/confusionmatrix

---


# confusionmatrix

> Metric `ConfusionMatrix` from `torchmetrics` (torchmetrics.ConfusionMatrix)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with ConfusionMatrix, or
mentions `torchmetrics.ConfusionMatrix` directly, or wants the standard torchmetrics implementation.

## Reference signature

```python
from torchmetrics import ConfusionMatrix

# ConfusionMatrix(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: Optional[int] = None, num_labels: Optional[int] = None, normalize: Optional[Literal['true', 'pred', 'all', 'none']] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> torchmetrics.metric.Metric
```

## Library docstring

```
Compute the `confusion matrix`_.

This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
:class:`~torchmetrics.classification.BinaryConfusionMatrix`,
:class:`~torchmetrics.classification.MulticlassConfusionMatrix` and
:class:`~torchmetrics.classification.MultilabelConfusionMatrix` for the specific details of each argument influence
and examples.

Legacy Example:
    >>> from torch import tensor
    >>> target = tensor([1, 1, 0, 0])
    >>> preds = tensor([0, 1, 0, 0])
    >>> confmat = ConfusionMatrix(task="binary", num_classes=2)
    >>> confmat(preds, target)
    tensor([[2, 0],
            [1, 1]])

    >>> target = tensor([2, 1, 0, 0])
    >>> preds = tensor([2, 1, 0, 1])
    >>> confmat = ConfusionMatrix(task="multiclass", num_classes=3)
    >>> confmat(preds, target)
    tensor([[1, 1, 0],
            [0, 1, 0],
            [0, 0, 1]])

    >>> target = tensor([[0, 1, 0], [1, 0, 1]])
    >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
    >>> confmat = ConfusionMatrix(task="multilabel", num_labels=3)
    >>> confmat(preds, target)
    tensor([[[1, 0], [0, 1]],
            [[1, 0], [1, 0]],
            [[0, 1], [0, 1]]])
```

## Quick recipe

```python
import torchmetrics as _m
score = _m.ConfusionMatrix(y_true, y_pred)
```

## Don'ts

- Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
- Always check the library version's argument order — sklearn is `(y_true, y_pred)` while torchmetrics is `(preds, target)`.