Jensen-Shannon Divergence (JS)
The Jensen-Shannon divergence (JS) measures how much the label distributions of different facets diverge from each other entropically. It is based on the Kullback-Leibler divergence, but it is symmetric.
The formula for the Jensen-Shannon divergence is as follows:
JS = ½*[KL(Pa || P) + KL(Pd || P)]
Where P = ½( Pa + Pd ), the average label distribution across facets a and d.
The range of JS values for binary, multicategory, continuous outcomes is [0, ln(2)).
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Values near zero mean the labels are similarly distributed.
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Positive values mean the label distributions diverge, the more positive the larger the divergence.
This metric indicates whether there is a big divergence in one of the labels across facets.
