zero_one_loss

sklearn.metrics.zero_one_loss(y_true, y_pred, *, normalize=True, sample_weight=None)

Zero-one classification loss.

If normalize is True, returns the fraction of misclassifications, else returns the number of misclassifications. The best performance is 0.

Read more in the User Guide.

Parameters:

y_true1d array-like, or label indicator array / sparse matrix

Ground truth (correct) labels. Sparse matrix is only supported when labels are of multilabel type.

y_pred1d array-like, or label indicator array / sparse matrix

Predicted labels, as returned by a classifier. Sparse matrix is only supported when labels are of multilabel type.

normalizebool, default=True

If False, return the number of misclassifications. Otherwise, return the fraction of misclassifications.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

lossfloat

If normalize == True, returns the fraction of misclassifications, else returns the number of misclassifications.

See also

accuracy_score

Compute the accuracy score. By default, the function will return the fraction of correct predictions divided by the total number of predictions.

hamming_loss

Compute the average Hamming loss or Hamming distance between two sets of samples.

jaccard_score

Compute the Jaccard similarity coefficient score.

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)
1.0

In the multilabel case with binary label indicators:

>>> import numpy as np
>>> zero_one_loss(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
0.5