sklearn.metrics.hamming_loss(y_true, y_pred, labels=None, sample_weight=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: 


Returns: 

See also
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 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.
[1]  Grigorios Tsoumakas, Ioannis Katakis. MultiLabel Classification: An Overview. International Journal of Data Warehousing & Mining, 3(3), 113, JulySeptember 2007. 
[2]  Wikipedia entry on the Hamming distance 
>>> 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
sklearn.metrics.hamming_loss
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http://scikitlearn.org/stable/modules/generated/sklearn.metrics.hamming_loss.html