Matthewscorrcoef

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

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

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).