# Binarystatscores

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

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

---


# binarystatscores

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import BinaryStatScores

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

## Library docstring

```
Compute true positives, false positives, true negatives, false negatives and the support for binary tasks.

Related to `Type I and Type II errors`_.

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

- ``preds`` (:class:`~torch.Tensor`): An int 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:

- ``bss`` (:class:`~torch.Tensor`): A tensor of shape ``(..., 5)``, where the last dimension corresponds
  to ``[tp, fp, tn, fn, sup]`` (``sup`` stands for support and equals ``tp + fn``). The shape
  depends on the ``multidim_average`` parameter:

  - If ``multidim_average`` is set to ``global``, the shape will be ``(5,)``
  - If ``multidim_average`` is set to ``samplewise``, the shape will be ``(N, 5)``

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:
    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 case are calculated over the additional dimensions.

    ignore_index:
        Specifies a target value that is ignored and does not contribute to the metric calculation
    validate_args: bool indicating if input arguments and tensors should be validated for correctness.
        Set to ``False`` for faster computations.
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

Example (preds is int tensor):
    >
```

## Quick recipe

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