---
title: accuracy_score
emoji: 🤗 
colorFrom: blue
colorTo: orange
tags:
- evaluate
- metric
- sklearn
description: >-
  "Accuracy classification score."
sdk: gradio
sdk_version: 3.12.0
app_file: app.py
pinned: false
---

This metric is part of the Scikit-learn integration into 🤗 Evaluate. You can find all available metrics in the [Scikit-learn organization](https://huggingface.co/scikit-learn) on the Hugging Face Hub.

<p align="center">
  <img src="https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/1280px-scikit-learn-logo.png" width="400"/>
</p>

# Metric Card for `sklearn.metrics.accuracy_score`




## Input Convention

To be consistent with the `evaluate` input conventions the scikit-learn inputs are renamed:

- `y_true`: `references`
- `y_pred`: `predictions`


## Usage

```python
import evaluate

metric = evaluate.load("sklearn/accuracy_score")
results = metric.compute(references=references, predictions=predictions)
```

## Description

Accuracy classification score.

    In multilabel classification, this function computes subset accuracy:
    the set of labels predicted for a sample must *exactly* match the
    corresponding set of labels in y_true.

    Read more in the :ref:`User Guide <accuracy_score>`.

    Parameters
    ----------
    y_true : 1d array-like, or label indicator array / sparse matrix
        Ground truth (correct) labels.

    y_pred : 1d array-like, or label indicator array / sparse matrix
        Predicted labels, as returned by a classifier.

    normalize : bool, default=True
        If ``False``, return the number of correctly classified samples.
        Otherwise, return the fraction of correctly classified samples.

    sample_weight : array-like of shape (n_samples,), default=None
        Sample weights.

    Returns
    -------
    score : float
        If ``normalize == True``, return the fraction of correctly
        classified samples (float), else returns the number of correctly
        classified samples (int).

        The best performance is 1 with ``normalize == True`` and the number
        of samples with ``normalize == False``.

    See Also
    --------
    balanced_accuracy_score : Compute the balanced accuracy to deal with
        imbalanced datasets.
    jaccard_score : Compute the Jaccard similarity coefficient score.
    hamming_loss : Compute the average Hamming loss or Hamming distance between
        two sets of samples.
    zero_one_loss : Compute the Zero-one classification loss. By default, the
        function will return the percentage of imperfectly predicted subsets.

    Notes
    -----
    In binary classification, this function is equal to the `jaccard_score`
    function.

    Examples
    --------
    >>> from sklearn.metrics import accuracy_score
    >>> y_pred = [0, 2, 1, 3]
    >>> y_true = [0, 1, 2, 3]
    >>> accuracy_score(y_true, y_pred)
    0.5
    >>> accuracy_score(y_true, y_pred, normalize=False)
    2

    In the multilabel case with binary label indicators:

    >>> import numpy as np
    >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
    0.5
    

## 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
- Docs: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html