---
title: MCC
datasets:
-  dataset
tags:
- evaluate
- metric
description: "Matthews correlation coefficient (MCC) is a correlation coefficient used in machine learning as a measure of the quality of binary and multiclass classifications."
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
---

# Metric Card for MCC



## Metric Description
*Give a brief overview of this metric, including what task(s) it is usually used for, if any.Matthews correlation coefficient (MCC) is a correlation coefficient used in machine learning as a measure of the quality of binary and multiclass classifications. MCC takes into account true and false positives and negatives and is generally regarded as a balanced metric that can be used even if the classes are of different sizes. It can be computed with the equation:
`MCC = (TP * TN - FP * FN) / sqrt((TP + FP)(TP + FN)(TN + FP)*(TN + FN))`
where TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives and FN is the number of false negatives.
## How to Use
*At minimum, this metric takes as input two lists, each containing ints: predictions and references.*

`
>>> mcc_metric = evaluate.load('mcc')
>>> results = mcc_metric.compute(references=[0, 1], predictions=[0, 1])
>>> print(results)
["{'mcc': 1.0}"] `



### Inputs

- **predictions** *(list of int): The predicted labels.*
- **references** *(list of int): The ground truth labels.*

### Output Values

**mcc(float)**: The Matthews correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. A higher MCC means a better quality of classification, 1 being a perfect prediction, 0 being a random prediction and -1 being a completely wrong prediction.
Output Example(s):
{'mcc': 1.0}



### Examples

Example 1 - A simple example with all correct predictions
>>> mcc_metric = evaluate.load('mcc')
>>> results = mcc_metric.compute(references=[1, 0, 1], predictions=[1, 0, 1])
>>> print(results)
{'mcc': 1.0}


Example 2 - A simple example with all incorrect predictions
>>> mcc_metric = evaluate.load('mcc')
>>> results = mcc_metric.compute(references=[1, 0, 1], predictions=[0, 1, 0])
>>> print(results)
{'mcc': -1.0}


Example 3 - A simple example with a random prediction
>>> mcc_metric = evaluate.load('mcc')
>>> results = mcc_metric.compute(references=[1, 0, 1], predictions=[1, 1, 0])
>>> print(results)
{'mcc': 0.0}

## Limitations and Bias
*Note any known limitations or biases that the metric has, with links and references if possible.*

## Citation
- **Sklearn** - *"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"*

## Further References