Matthewscorrcoef

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

# matthewscorrcoef

> Metric `MatthewsCorrCoef` from `torchmetrics` (torchmetrics.MatthewsCorrCoef)

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import MatthewsCorrCoef

# MatthewsCorrCoef(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: Optional[int] = None, num_labels: Optional[int] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> torchmetrics.metric.Metric
```

## Library docstring

```
Calculate `Matthews correlation coefficient`_ .

This metric measures the general correlation or quality of a classification.

This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
:class:`~torchmetrics.classification.BinaryMatthewsCorrCoef`,
:class:`~torchmetrics.classification.MulticlassMatthewsCorrCoef` and
:class:`~torchmetrics.classification.MultilabelMatthewsCorrCoef` for the specific details of each argument influence
and examples.

Legacy Example:
    >>> from torch import tensor
    >>> target = tensor([1, 1, 0, 0])
    >>> preds = tensor([0, 1, 0, 0])
    >>> matthews_corrcoef = MatthewsCorrCoef(task='binary')
    >>> matthews_corrcoef(preds, target)
    tensor(0.5774)
```

## Quick recipe

```python
import torchmetrics as _m
score = _m.MatthewsCorrCoef(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)`.