Lift Score (LS)

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

• Higher is better (No best value), Range = [0, +inf)

• http://rasbt.github.io/mlxtend/user_guide/evaluate/lift_score/

• https://neptune.ai/blog/evaluation-metrics-binary-classification

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)

print(cm.lift_score(average=None))
print(cm.LS(average="micro"))
print(cm.LS(average="macro"))
print(cm.LS(average="weighted"))