KL divergence can not be Distance metric

This blog post discusses why KL divergence is not a valid distance metric. It explains the properties of distance metrics, highlighting that KL divergence is asymmetric, which means the distance from A to B is not the same as from B to A, violating a key property of distance metrics. The post emphasizes the importance of understanding the characteristics of different metrics in the context of machine learning and statistics, which can help developers and researchers choose the appropriate metric for their work.