README.md

---
title: ccc
tags:
- evaluate
- metric
description: "Concordance correlation coefficient"
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
---

# Metric Card for CCC

## Metric Description
The concordance correlation coefficient measures the agreement between two sets of
values. It is often used as a measure of inter-rater agreement when ratings have
continuous values.

## How to Use
The inputs are two sequences of floating point values. For example:
```python
ccc_metric = evaluate.load("agkphysics/ccc")
results = ccc_metric.compute(references=[0.2, 0.1], predictions=[0.1, 0.2])
```

### Inputs
- **predictions** (list of float): model predictions
- **references** (list of float): reference labels

### Output Values
- `ccc`: the concordance correlation coefficient. This is a value between -1 (perfect
anti-agreement) and 1 (perfect agreement), with 0 indicating no agreement.

### Examples
```python
>>> ccc_metric = evaluate.load("agkphysics/ccc")
>>> results = ccc_metric.compute(references=[0.2, 0.1], predictions=[0.1, 0.2])
>>> print(results)
{'ccc': -1.0}
>>> results = ccc_metric.compute(references=[0.1, 0.2], predictions=[0.1, 0.2])
>>> print(results)
{'ccc': 1.0}
>>> results = ccc_metric.compute(references=[0.1, 0.3], predictions=[0.1, 0.2])
>>> print(results)
{'ccc': 0.666666641831399}
```

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

## Citation
```bibtex
@article{linConcordanceCorrelationCoefficient1989,
  title = {A {{Concordance Correlation Coefficient}} to {{Evaluate Reproducibility}}},
  author = {Lin, Lawrence I-Kuei},
  year = {1989},
  journal = {Biometrics},
  volume = {45},
  number = {1},
  pages = {255--268},
  publisher = {{International Biometric Society}},
  issn = {0006-341X},
  url = {https://www.jstor.org/stable/2532051},
  doi = {10.2307/2532051}
}
```

## Further References
Wikipedia: https://en.wikipedia.org/wiki/Concordance_correlation_coefficient