sklearn.metrics.hamming_loss

sklearn.metrics.hamming_loss(y_true, y_pred, classes=None, sample_weight=None)

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 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.

classes : array, shape = [n_labels], optional

Integer array of labels.

sample_weight : array-like of shape = [n_samples], optional

Sample weights.

Returns:

loss : float or int,

Return the average Hamming loss between element of y_true and y_pred.

See also

accuracy_score, jaccard_similarity_score, zero_one_loss

Notes

In multiclass classification, the Hamming loss correspond to the Hamming distance between y_true and y_pred which is equivalent to the subset zero_one_loss function.

In multilabel classification, the Hamming loss is different from the subset zero-one loss. The zero-one 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 zero-one loss. When normalized over samples, the Hamming loss is always between 0 and 1.

References

[R173]

Grigorios Tsoumakas, Ioannis Katakis. Multi-Label Classification: An Overview. International Journal of Data Warehousing & Mining, 3(3), 1-13, July-September 2007.

[R174]

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

Examples using sklearn.metrics.hamming_loss

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