# Multiclasshammingdistance

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

- Skill: `qhjqhj00/multiclasshammingdistance` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/multiclasshammingdistance`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/multiclasshammingdistance/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/multiclasshammingdistance

---


# multiclasshammingdistance

> Metric `MulticlassHammingDistance` from `torchmetrics` (torchmetrics.classification.MulticlassHammingDistance)

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import MulticlassHammingDistance

# MulticlassHammingDistance(num_classes: Optional[int] = None, top_k: int = 1, average: Optional[Literal['micro', 'macro', 'weighted', 'none']] = 'macro', multidim_average: Literal['global', 'samplewise'] = 'global', ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> None
```

## Library docstring

```
Compute the average `Hamming distance`_ (also known as Hamming loss) for multiclass tasks.

.. math::
    \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})

Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
tensor.

As input to ``forward`` and ``update`` the metric accepts the following input:

- ``preds`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` or float tensor of shape ``(N, C, ..)``.
  If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert
  probabilities/logits into an int tensor.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.

As output to ``forward`` and ``compute`` the metric returns the following output:

- ``mchd`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``average`` and
  ``multidim_average`` arguments:

    - If ``multidim_average`` is set to ``global``:

      - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
      - If ``average=None/'none'``, the shape will be ``(C,)``

    - If ``multidim_average`` is set to ``samplewise``:

      - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
      - If ``average=None/'none'``, the shape will be ``(N, C)``

If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
which the reduction will then be applied over instead of the sample dimension ``N``.

Args:
    num_classes: Integer specifying the number of classes
    average:
        Defines the reduction that is applied over labels. Should be one of the following:

        - ``micro``: Sum statistics over all labels
        - ``macro``: Calculate statistics for each label and average them
        - ``weighted``: calculates statistics for each label and computes weighted average using their support
        - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction
    top_k:
        Number of highest probability or logit score
```

## Quick recipe

```python
import torchmetrics.classification as _m
score = _m.MulticlassHammingDistance(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)`.