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sklearn.metrics.zero_one_loss(y_true, y_pred, normalize=True, sample_weight=None)
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Zero-one classification loss.
If normalize is
True
, return the fraction of misclassifications (float), else it returns the number of misclassifications (int). The best performance is 0.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 misclassifications. Otherwise, return the fraction of misclassifications.sample_weight : array-like of shape = [n_samples], optional
Sample weights.
Returns: loss : float or int,
If
normalize == True
, return the fraction of misclassifications (float), else it returns the number of misclassifications (int).See also
Notes
In multilabel classification, the zero_one_loss function corresponds to the subset zero-one loss: for each sample, the entire set of labels must be correctly predicted, otherwise the loss for that sample is equal to one.
Examples
>>> from sklearn.metrics import zero_one_loss >>> y_pred = [1, 2, 3, 4] >>> y_true = [2, 2, 3, 4] >>> zero_one_loss(y_true, y_pred) 0.25 >>> zero_one_loss(y_true, y_pred, normalize=False) 1
In the multilabel case with binary label indicators:
>>> zero_one_loss(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) 0.5
sklearn.metrics.zero_one_loss()
Examples using
2017-01-15 04:26:44
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