Multilabeleer

---
name: multilabeleer
description: Compute the MultilabelEER metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute MultilabelEER, or asks how to score with MultilabelEER.
metadata:
  skill_kind: metric
  source_lib: torchmetrics
  import_path: torchmetrics.classification.MultilabelEER
  source: library_introspection
---

# multilabeleer

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import MultilabelEER

# MultilabelEER(num_labels: int, thresholds: Union[int, list[float], torch.Tensor, NoneType] = 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 sigmoid per element.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)`` containing ground truth labels, and
  therefore only contain {0,1} values (except if `ignore_index` is specified).

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

- ``ml_eer`` (:class:`~torch.Tensor`): A 1d tensor of shape (n_classes, ) will be returned with eer score per label.

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_{labels})` (constant memory).

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

        - ``micro``: Sum score over all labels
        - ``macro``: Calculate score for each label and average them
        - ``weighted``: calculates score for each label and computes weighted average using their support
        - ``"none"`` or ``None``: calculates score for each label and applies no reduction

    thresholds: Can be one of:

        - 
```

## Quick recipe

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