sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)
[source]
Accuracy classification score.
In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.
Read more in the User Guide.
Parameters: 


Returns: 

See also
In binary and multiclass classification, this function is equal to the jaccard_similarity_score
function.
>>> import numpy as np >>> from sklearn.metrics import accuracy_score >>> y_pred = [0, 2, 1, 3] >>> y_true = [0, 1, 2, 3] >>> accuracy_score(y_true, y_pred) 0.5 >>> accuracy_score(y_true, y_pred, normalize=False) 2
In the multilabel case with binary label indicators:
>>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) 0.5
sklearn.metrics.accuracy_score
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http://scikitlearn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html