# Matthews Corrcoef

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

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

---


# matthews-corrcoef

> Metric `matthews_corrcoef` from `scikit-learn` (sklearn.metrics.matthews_corrcoef)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with matthews_corrcoef, or
mentions `sklearn.metrics.matthews_corrcoef` directly, or wants the standard scikit-learn implementation.

## Reference signature

```python
from sklearn.metrics import matthews_corrcoef

# matthews_corrcoef(y_true, y_pred, *, sample_weight=None)
```

## Library docstring

```
Compute the Matthews correlation coefficient (MCC).

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]

Binary and multiclass labels are supported.  Only in the binary case does
this relate to information about true and false positives and negatives.
See references below.

Read more in the :ref:`User Guide <matthews_corrcoef>`.

Parameters
----------
y_true : array-like of shape (n_samples,)
    Ground truth (correct) target values.

y_pred : array-like of shape (n_samples,)
    Estimated targets as returned by a classifier.

sample_weight : array-like of shape (n_samples,), default=None
    Sample weights.

    .. versionadded:: 0.18

Returns
-------
mcc : float
    The Matthews correlation coefficient (+1 represents a perfect
    prediction, 0 an average random prediction and -1 and inverse
    prediction).

References
----------
.. [1] :doi:`Baldi, Brunak, Chauvin, Andersen and Nielsen, (2000). Assessing the
   accuracy of prediction algorithms for classification: an overview.
   <10.1093/bioinformatics/16.5.412>`

.. [2] `Wikipedia entry for the Matthews Correlation Coefficient (phi coefficient)
   <https://en.wikipedia.org/wiki/Phi_coefficient>`_.

.. [3] `Gorodkin, (2004). Comparing two K-category assignments by a
    K-category correlation coefficient
    <https://www.sciencedirect.com/science/article/pii/S1476927104000799>`_.

.. [4] `Jurman, Riccadonna, Furlanello, (2012). A Comparison of MCC and CEN
    Error Measures in MultiClass Prediction
    <https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041882>`_.

Examples
--------
>>> from sklearn.metrics import matthews_corrcoef
>>> y_true = [+1, +1, +1, -1]
>>> y_pred = [+1, -1
```

## Quick recipe

```python
import sklearn.metrics as _m
score = _m.matthews_corrcoef(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)`.