Accuracy Score

accuracy-score

Metric accuracy_score from scikit-learn (sklearn.metrics.accuracy_score)

When to invoke this skill

The user has predictions + ground truth and asks to evaluate with accuracy_score, or mentions sklearn.metrics.accuracy_score directly, or wants the standard scikit-learn implementation.

Reference signature

from sklearn.metrics import accuracy_score

# accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None)

Library docstring

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. Sparse matrix is only supported when
    labels are of :term:`multilabel` type.

y_pred : 1d array-like, or label indicator array / sparse matrix
    Predicted labels, as returned by a classifier. Sparse matrix is only
    supported when labels are of :term:`multilabel` type.

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``, returns the fraction of correctly classified samples,
    else returns the number of correctly classified samples.

    The best performance is 1.0 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.

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)
2.0

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

Quick recipe

import sklearn.metrics as _m
score = _m.accuracy_score(y_true, y_pred)

Don'ts

• Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
• Always check the library version's argument order — sklearn is (y_true, y_pred) while torchmetrics is (preds, target).