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---
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 |