README.md

---
title: HTER
emoji: 🤗 
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
tags:
- evaluate
- metric
description: >-
  HTER (Half Total Error Rate) is a metric that combines the False Accept Rate (FAR) and False Reject Rate (FRR) to provide a comprehensive evaluation of a system's performance. It can be computed with:
  HTER = (FAR + FRR) / 2
  Where:
  FAR (False Accept Rate) = FP / (FP + TN)
  FRR (False Reject Rate) = FN / (FN + TP)
  TP: True positive
  TN: True negative
  FP: False positive
  FN: False negative
---

# Metric Card for HTER


## Metric Description

HTER (Half Total Error Rate) is a metric that combines the False Accept Rate (FAR) and False Reject Rate (FRR) to provide a comprehensive evaluation of a system's performance. It can be computed with:
  
HTER = (FAR + FRR) / 2
  
Where:

  FAR (False Accept Rate) = FP / (FP + TN)

  FRR (False Reject Rate) = FN / (FN + TP)

  TP: True positive

  TN: True negative

  FP: False positive

  FN: False negative



## How to Use

At minimum, this metric requires predictions and references as inputs.

```python
>>> hter_metric = evaluate.load("murinj/hter")
>>> results = hter_metric.compute(references=[0, 0], predictions=[0, 1])
>>> print(results)
{'HTER': 0.25}
```


### Inputs
- **predictions** (`list` of `int`): Predicted labels.
- **references** (`list` of `int`): Ground truth labels.

[//]: # (- **normalize** (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.)

[//]: # (- **sample_weight** (`list` of `float`): Sample weights Defaults to None.)


### Output Values
- **HTER**(`float` or `int`): HTER score. Minimum possible value is 0. Maximum possible value is 1.0.

Output Example(s):
```python
{'HTER': 0.0}
```

This metric outputs a dictionary, containing the HTER score.


[//]: # (## Citation(s))

[//]: # (```bibtex)

[//]: # ()
[//]: # (```)


## Further References