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lvwerra/accuracy_score

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App README

This metric is part of the Scikit-learn integration into 🤗 Evaluate. You can find all available metrics in the Scikit-learn organization on the Hugging Face Hub.

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.

sampleweight : array-like of shape (nsamples,), 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 -------- balancedaccuracyscore : Compute the balanced accuracy to deal with imbalanced datasets. jaccardscore : Compute the Jaccard similarity coefficient score. hammingloss : Compute the average Hamming loss or Hamming distance between two sets of samples. zerooneloss : 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 accuracyscore >> ypred = [0, 2, 1, 3] >> ytrue = [0, 1, 2, 3] >> accuracyscore(ytrue, ypred)

0.5

>> accuracyscore(ytrue, y_pred, normalize=False)

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