sklearn.metrics
.balanced_accuracy_score¶
- sklearn.metrics.balanced_accuracy_score(y_true, y_pred, *, sample_weight=None, adjusted=False)[source]¶
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 User Guide.
New in version 0.20.
- Parameters:
- y_true1d array-like
Ground truth (correct) target values.
- y_pred1d array-like
Estimated targets as returned by a classifier.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- adjustedbool, 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_accuracyfloat
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
accuracy_score
with class-balanced sample weights, and shares desirable properties with the binary case. See the User Guide.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.
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_true, y_pred) 0.625