alt Hacker NewsThere are many applications. I mainly see it used for detecting drift in datasets for ML models. It has a nice benefit over the KL divergence in the case where the two distributions you're measuring have no overlap (KL won't compute, but JS will just return 0). Also, when taking its square root you get a distance rather than a divergence which allows you to compare it to JSD measurements of other distributions.
> Also, when taking its square root you get a distance
Easy conversion into a distance metric is hugely valuable to making the property amenable to KNN-based dimensionality reduction algos (and I'm sure other things I don't understand, as a non-mathematician)
Here's a library that the creator of UMAP provides (UMAP being a workhorse of dimensional reduction algos), for doing approx nearest neighbor search: https://pynndescent.readthedocs.io/en/latest/api.html#pynnde...