Balanced Accuracy Score

balanced-accuracy-score

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

When to invoke this skill

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

Reference signature

from sklearn.metrics import balanced_accuracy_score

# balanced_accuracy_score(y_true, y_pred, *, sample_weight=None, adjusted=False)

Library docstring

Compute the balanced accuracy.

The balanced accuracy in binary and multiclass classification problems to
deal with imbalanced datasets. It is defined as the average of recall
obtained on each class.

The best value is 1 and the worst value is 0 when ``adjusted=False``.

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

.. versionadded:: 0.20

Parameters
----------
y_true : array-like of shape (n_samples,)
    Ground truth (correct) target values.

y_pred : array-like of shape (n_samples,)
    Estimated targets as returned by a classifier.

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

adjusted : bool, default=False
    When true, the result is adjusted for chance, so that random
    performance would score 0, while keeping perfect performance at a score
    of 1.

Returns
-------
balanced_accuracy : float
    Balanced accuracy score.

See Also
--------
average_precision_score : Compute average precision (AP) from prediction
    scores.
precision_score : Compute the precision score.
recall_score : Compute the recall score.
roc_auc_score : Compute Area Under the Receiver Operating Characteristic
    Curve (ROC AUC) from prediction scores.

Notes
-----
Some literature promotes alternative definitions of balanced accuracy. Our
definition is equivalent to :func:`accuracy_score` with class-balanced
sample weights, and shares desirable properties with the binary case.
See the :ref:`User Guide <balanced_accuracy_score>`.

References
----------
.. [1] Brodersen, K.H.; Ong, C.S.; Stephan, K.E.; Buhmann, J.M. (2010).
       The balanced accuracy and its posterior distribution.
       Proceedings of the 20th International Conference on Pattern
       Recognition, 3121-24.
.. [2] John. D. Kelleher, Brian Mac Namee, Aoife D'Arcy, (2015).
       `Fundamentals of Machine Learning for Predictive Data Analytics:
       Algorithms, Worked Examples, and Case Studies
       <https://mitpress.mit.edu/books/fundamentals-machine-learning-predictive-data-analytics>`_.

Examples
--------
>>> from sklearn.metrics import balanced_accuracy_score
>>> y_true = [0, 1, 0, 0, 1, 0]
>>> y_pred = [0, 1, 0, 0, 0, 1]
>>> balanced_accuracy_score(y_t

Quick recipe

import sklearn.metrics as _m
score = _m.balanced_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).