Hamming Loss (HL)

The Hamming loss is the fraction of labels that are incorrectly predicted.

In the multi-class and multi-label case, this is the average of the HL score of each class with weighting depending on the average parameter.

• Best possible score is 0.0, lower value is better. Range = [0, 1]

• https://scikit-learn.org/stable/modules/generated/sklearn.metrics.hamming_loss.html#sklearn.metrics.hamming_loss

Example:

from numpy import array
from permetrics.classification import ClassificationMetric

## For integer labels or categorical labels
y_true = [0, 1, 0, 0, 1, 0]
y_pred = [0, 1, 0, 0, 0, 1]

# y_true = ["cat", "ant", "cat", "cat", "ant", "bird", "bird", "bird"]
# y_pred = ["ant", "ant", "cat", "cat", "ant", "cat", "bird", "ant"]

cm = ClassificationMetric(y_true, y_pred, decimal = 5)

print(cm.hamming_loss(average=None))
print(cm.hl(average="micro"))
print(cm.HL(average="macro"))
print(cm.HL(average="weighted"))