---
title: Specificity
tags:
- evaluate
- metric
description: "Specificity is the fraction of the negatives examples that were correctly labeled by the model as negatives. It can be computed with the equation: Specificity = TN / (TN + FP) Where TN is the true negatives and FP is the false positives."
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
---

# Metric Card for Specificity

## Metric Description

Specificity is the fraction of the negatives examples that were correctly labeled by the model as negatives. It can be computed with the equation:
$$\text{Specificity} = \frac{TN}{TN + FP}$$
Where \\(TN\\) is the true negatives and \\(FP\\) is the false positives.

## How to Use

At minimum, this metric takes as input two lists, each containing ints: predictions and references.

```python
>>> specificity_metric = evaluate.load('nevikw39/specificity')
>>> results = specificity_metric.compute(references=[0, 1], predictions=[0, 1])
>>> print(results)
["{'specificity': 1.0}"]
```

### Inputs

- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **neg_label** (`int`): The class label to use as the 'negative class' when calculating the specificity. Defaults to `0`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
    - `'binary'`: Only report results for the class specified by `neg_label`. This is applicable only if the target labels and predictions are binary.
    - `'micro'`: Calculate metrics globally by counting the total true negatives, false positives, and false negatives.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between sensitivity and specificity.
    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

### Output Values

- **specificity** (`float`, or `array` of `float`): Either the general specificity score, or the specificity scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher specificity means that more of the positive examples have been labeled correctly. Therefore, a higher specificity is generally considered better.

#### Values from Popular Papers

### Examples

Example 1-A simple example with some errors
```python
    >>> specificity_metric = evaluate.load('nevikw39/specificity')
    >>> results = specificity_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
    >>> print(results)
    {'specificity': 0.5}
```
Example 2-The same example as Example 1, but with `neg_label=1` instead of the default `neg_label=0`.
```python
    >>> specificity_metric = evaluate.load('nevikw39/specificity')
    >>> results = specificity_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], neg_label=1)
    >>> print(results)
    {'specificity': 0.6666666666666666}
```
Example 3-The same example as Example 1, but with `sample_weight` included.
```python
    >>> specificity_metric = evaluate.load('nevikw39/specificity')
    >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
    >>> results = specificity_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
    >>> print(results)
    {'specificity': 0.8181818181818181}
```
Example 4-A multiclass example, using different averages.
```python
    >>> specificity_metric = evaluate.load('nevikw39/specificity')
    >>> predictions = [0, 2, 1, 0, 0, 1]
    >>> references = [0, 1, 2, 0, 1, 2]
    >>> results = specificity_metric.compute(predictions=predictions, references=references, average='macro')
    >>> print(results)
    {'specificity': 0.3333333333333333}
    >>> results = specificity_metric.compute(predictions=predictions, references=references, average='micro')
    >>> print(results)
    {'specificity': 0.3333333333333333}
    >>> results = specificity_metric.compute(predictions=predictions, references=references, average='weighted')
    >>> print(results)
    {'specificity': 0.3333333333333333}
    >>> results = specificity_metric.compute(predictions=predictions, references=references, average=None)
    >>> print(results)
    {'specificity': array([1., 0., 0.])}
```

## Limitations and Bias

## Citation

```
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}}
```

## Further References