sklearn.metrics
.hamming_loss¶

sklearn.metrics.
hamming_loss
(y_true, y_pred, labels=None, sample_weight=None, classes=None)[source]¶ Compute the average Hamming loss.
The Hamming loss is the fraction of labels that are incorrectly predicted.
Read more in the User Guide.
Parameters: y_true : 1d arraylike, or label indicator array / sparse matrix
Ground truth (correct) labels.
y_pred : 1d arraylike, or label indicator array / sparse matrix
Predicted labels, as returned by a classifier.
labels : array, shape = [n_labels], optional (default=None)
Integer array of labels. If not provided, labels will be inferred from y_true and y_pred.
New in version 0.18.
sample_weight : arraylike of shape = [n_samples], optional
Sample weights.
New in version 0.18.
classes : array, shape = [n_labels], optional
Integer array of labels.
Deprecated since version 0.18: This parameter has been deprecated in favor of
labels
in version 0.18 and will be removed in 0.20. Uselabels
instead.Returns: loss : float or int,
Return the average Hamming loss between element of
y_true
andy_pred
.See also
Notes
In multiclass classification, the Hamming loss correspond to the Hamming distance between
y_true
andy_pred
which is equivalent to the subsetzero_one_loss
function.In multilabel classification, the Hamming loss is different from the subset zeroone loss. The zeroone loss considers the entire set of labels for a given sample incorrect if it does entirely match the true set of labels. Hamming loss is more forgiving in that it penalizes the individual labels.
The Hamming loss is upperbounded by the subset zeroone loss. When normalized over samples, the Hamming loss is always between 0 and 1.
References
[R220] Grigorios Tsoumakas, Ioannis Katakis. MultiLabel Classification: An Overview. International Journal of Data Warehousing & Mining, 3(3), 113, JulySeptember 2007. [R221] Wikipedia entry on the Hamming distance Examples
>>> from sklearn.metrics import hamming_loss >>> y_pred = [1, 2, 3, 4] >>> y_true = [2, 2, 3, 4] >>> hamming_loss(y_true, y_pred) 0.25
In the multilabel case with binary label indicators:
>>> hamming_loss(np.array([[0, 1], [1, 1]]), np.zeros((2, 2))) 0.75