# Multiclasseer

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

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

---


# multiclasseer

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import MulticlassEER

# MulticlassEER(num_classes: int, thresholds: Union[int, list[float], torch.Tensor, NoneType] = None, average: Optional[Literal['micro', 'macro']] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> None
```

## Library docstring

```
Compute Equal Error Rate (EER) for multiclass classification task.

.. math::
    \text{EER} = \frac{\text{FAR} + (1 - \text{FRR})}{2}, \text{where} \min_t abs(FAR_t-FRR_t)

The Equal Error Rate (EER) is the point where the False Positive Rate (FPR) and True Positive Rate (TPR) are
equal, or in practise minimized. A lower EER value signifies higher system accuracy.

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

    - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)`` containing probabilities or logits
      for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto
      apply softmax per sample.
    - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` containing ground truth labels, and
      therefore only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified).

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

    - ``mc_eer`` (:class:`~torch.Tensor`): If `average=None` then a 1d tensor of shape (n_classes, ) will
      be returned with eer score per class. If `average="macro"|"micro"` then a single scalar will be returned.

Additional dimension ``...`` will be flattened into the batch dimension.

The implementation both supports calculating the metric in a non-binned but accurate version and a
binned version that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will
activate the non-binned version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the
`thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory).

Args:
    num_classes: Integer specifying the number of classes
    thresholds: Can be one of:

        - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
          all the data. Most accurate but also most memory consuming approach.
        - If set to an `int` (larger than 1), will use that number of thresholds linearly s
```

## Quick recipe

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