Binaryfbetascore

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

# binaryfbetascore

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import BinaryFBetaScore

# BinaryFBetaScore(beta: float, threshold: float = 0.5, multidim_average: Literal['global', 'samplewise'] = 'global', ignore_index: Optional[int] = None, validate_args: bool = True, zero_division: float = 0, **kwargs: Any) -> None
```

## Library docstring

```
Compute `F-score`_ metric for binary tasks.

.. math::
    F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}}
    {(\beta^2 * \text{precision}) + \text{recall}}

The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0`
where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false
positives and false negatives respectively. If this case is encountered a score of `zero_division`
(0 or 1, default is 0) is returned.

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

- ``preds`` (:class:`~torch.Tensor`): An int tensor or float tensor of shape ``(N, ...)``. If preds is a floating
  point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid
  per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.

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

- ``bfbs`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` argument:

    - If ``multidim_average`` is set to ``global`` the output will be a scalar tensor
    - If ``multidim_average`` is set to ``samplewise`` the output will be a tensor of shape ``(N,)`` consisting of
      a scalar value per sample.

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:
    beta: Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight
    threshold: Threshold for transforming probability to binary {0,1} predictions
    multidim_average:
        Defines how additionally dimensions ``...`` should be handled. Should be one of the following:

        - ``global``: Additional dimensions are flatted along the batch dimension
        - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
          The statistics in this ca
```

## Quick recipe

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