Binaryeer

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

# binaryeer

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import BinaryEER

# BinaryEER(thresholds: Union[int, list[float], torch.Tensor, NoneType] = None, ignore_index: Optional[int] = None, validate_args: bool = True, normalization: Optional[Literal['sigmoid', 'softmax']] = 'sigmoid', **kwargs: Any) -> None
```

## Library docstring

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

.. math::
    \text{EER} = \frac{\text{FAR} + \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, ...)`` 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, ...)`` containing ground truth labels, and
  therefore only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the
  positive class.

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

- ``b_eer`` (:class:`~torch.Tensor`): A single scalar with the eer score.

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})` (constant memory).

Args:
    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 spaced from
          0 to 1 as bins for the calculation.
        - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
        - If set to an 1d `tensor` of floats, will 
```

## Quick recipe

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