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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: 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.
normalize : bool, optional (default=True)
If
False
, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples.sample_weight : array-like of shape = [n_samples], optional
Sample weights.
Returns: score : float
If
normalize == True
, return the correctly classified samples (float), else it returns the number of correctly classified samples (int).The best performance is 1 with
normalize == True
and the number of samples withnormalize == False
.See also
Notes
In binary and multiclass classification, this function is equal to the
jaccard_similarity_score
function.Examples
>>> 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()
Examples using
2017-01-15 04:26:14
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