README.md

---
title: classification_report
tags:
- evaluate
- metric
description: >-
  Build a text report showing the main classification metrics that are accuracy, precision, recall and F1.
sdk: gradio
sdk_version: 3.14.0
app_file: app.py
pinned: false
license: apache-2.0
---

# Metric Card for classification_report


## Metric Description

Build a text report showing the main classification metrics that are accuracy, precision, recall and F1.


## How to Use

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

```python
>>> classification_report_metric = evaluate.load("bstrai/classification_report")
>>> results = classification_report_metric.compute(references=[0, 1], predictions=[0, 1])
>>> print(results)
{'0': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1}, '1': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1}, 'accuracy': 1.0, 'macro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2}, 'weighted avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2}}
```


### Inputs
- **predictions** (`list` of `int`): Predicted labels.
- **references** (`list` of `int`): Ground truth labels.
- **labels** (`list` of `int`): Optional list of label indices to include in the report. Defaults to None.
- **target_names** (`list` of `str`): Optional display names matching the labels (same order). Defaults to None.
- **sample_weight** (`list` of `float`): Sample weights. Defaults to None.
- **digits** (`int`): Number of digits for formatting output floating point values. When output_dict is True, this will be ignored and the returned values will not be rounded. Defaults to 2.
- **zero_division** (`warn`, `0` or `1`): Sets the value to return when there is a zero division. If set to “warn”, this acts as 0, but warnings are also raised. Defaults to `warn`.


### Output Values
- report (`str` or `dict`): Text summary of the precision, recall, F1 score for each class. Dictionary returned if output_dict is True. Dictionary has the following structure:
        ```
        {'label 1': {'precision':0.5,
             'recall':1.0,
             'f1-score':0.67,
             'support':1},
         'label 2': { ... },
             ...
        }
        ```
        The reported averages include macro average (averaging the unweighted mean per label), weighted average (averaging the support-weighted mean per label), and sample average (only for multilabel classification). Micro average (averaging the total true positives, false negatives and false positives) is only shown for multi-label or multi-class with a subset of classes, because it corresponds to accuracy otherwise and would be the same for all metrics. See also precision_recall_fscore_support for more details on averages.
        Note that in binary classification, recall of the positive class is also known as “sensitivity”; recall of the negative class is “specificity”.

Output Example(s):
```python
{'0': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1}, '1': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1}, 'accuracy': 1.0, 'macro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2}, 'weighted avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2}}
```

### Examples

Simple Example:
```python
>>> classification_report_metric = evaluate.load("bstrai/classification_report")
>>> results = classification_report_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
>>> print(results)
{'0': {'precision': 0.5, 'recall': 0.5, 'f1-score': 0.5, 'support': 2}, '1': {'precision': 0.6666666666666666, 'recall': 1.0, 'f1-score': 0.8, 'support': 2}, '2': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 2}, 'accuracy': 0.5, 'macro avg': {'precision': 0.38888888888888884, 'recall': 0.5, 'f1-score': 0.43333333333333335, 'support': 6}, 'weighted avg': {'precision': 0.38888888888888884, 'recall': 0.5, 'f1-score': 0.43333333333333335, 'support': 6}}
```

## Citation
```bibtex
@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