What does KL divergence help measure in machine learning?

KL divergence, or Kullback-Leibler divergence, is a statistical measure that quantifies how one probability distribution diverges from a second, expected probability distribution. In the context of machine learning, KL divergence is often used to compare two different probability distributions, such as the true distribution of data and the predicted distribution generated by a model.

When measuring similarity between distributions, KL divergence essentially calculates the information lost when one distribution is used to approximate another. A smaller KL divergence value indicates that the two distributions are more similar, while a larger value indicates greater differences. This property makes KL divergence highly valuable in areas such as variational inference, where the goal is to approximate complex distributions with simpler ones.

This understanding of KL divergence directly ties it to the comparison of probability distributions, which is central to probabilistic models in machine learning. Therefore, the correct choice highlights the fundamental purpose of KL divergence in this field.