sklearn.metrics.zero_one_loss¶
- sklearn.metrics.zero_one_loss(y_true, y_pred, normalize=True)¶
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.
Parameters : y_true : array-like or list of labels or label indicator matrix
Ground truth (correct) labels.
y_pred : array-like or list of labels or label indicator 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.
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 indicator format:
>>> zero_one_loss(np.array([[0.0, 1.0], [1.0, 1.0]]), np.ones((2, 2))) 0.5
and with a list of labels format:
>>> zero_one_loss([(1, ), (3, )], [(1, 2), tuple()]) 1.0