# Binarycohenkappa

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

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

---


# binarycohenkappa

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import BinaryCohenKappa

# BinaryCohenKappa(threshold: float = 0.5, ignore_index: Optional[int] = None, weights: Optional[Literal['linear', 'quadratic', 'none']] = None, validate_args: bool = True, **kwargs: Any) -> None
```

## Library docstring

```
Calculate `Cohen's kappa score`_ that measures inter-annotator agreement for binary tasks.

.. math::
    \kappa = (p_o - p_e) / (1 - p_e)

where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is
the expected agreement when both annotators assign labels randomly. Note that
:math:`p_e` is estimated using a per-annotator empirical prior over the
class labels.

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

- ``preds`` (:class:`~torch.Tensor`): A 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, ...)``.

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

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

- ``bc_kappa`` (:class:`~torch.Tensor`): A tensor containing cohen kappa score

Args:
    threshold: Threshold for transforming probability to binary (0,1) predictions
    ignore_index:
        Specifies a target value that is ignored and does not contribute to the metric calculation
    weights: Weighting type to calculate the score. Choose from:

        - ``None`` or ``'none'``: no weighting
        - ``'linear'``: linear weighting
        - ``'quadratic'``: quadratic weighting

    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):
    >>> from torch import tensor
    >>> from torchmetrics.classification import BinaryCohenKappa
    >>> target = tensor([1, 1, 0, 0])
    >>> preds = tensor([0, 1, 0, 0])
    >>> metric = BinaryCohenKappa()
    >>> metric(preds, target)
    tensor(0.5000)

Example (preds is float tensor):
    >>> from torchmetrics.classification import BinaryCohenKappa
    >>> target = tensor([1, 1, 0, 0])
    >>> preds = ten
```

## Quick recipe

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