WHAT IS KL IN MATH!?

In mathematics, KL often refers to the Kullback-Leibler divergence, a measure of how one probability distribution diverges from a second, expected probability distribution. It quantifies the information lost when approximating one distribution with another. KL divergence is commonly used in statistics, machine learning, and information theory to assess model performance and data distributions.