Member-only story
Cross Examination On Data
On KL Divergence (Kullback-Leibler Divergence) and Cross-Entropy
10 min readJun 10, 2025
KL Divergence (Kullback-Leibler Divergence) and Cross-Entropy are closely related concepts in information theory and machine learning, particularly in classification and density estimation tasks. Here’s a breakdown of their advantages and disadvantages relative to each other.
✅ Advantages of KL Divergence over Cross-Entropy
1. Explicit Comparison of Distributions
- KL Divergence directly measures how one probability distribution (usually the true distribution) diverges from another (the predicted distribution).➜➤ useful when comparing or evaluating models that output probability distributions, such as in variational inference.2. Interpretability in Terms of Information Loss
- KL Divergence quantifies the expected…