How do Bayesian and machine-learning filters influence placement?

Bayesian filters calculate the probability a message is spam based on word frequencies. They learn from labeled examples of spam and ham. Classic technique, still used in many systems.

Machine learning filters extend far beyond word analysis. They evaluate hundreds of signals: sender behavior, authentication, engagement patterns, URL reputation, sending patterns, device characteristics, and more.

Modern placement decisions combine both approaches. Bayesian analysis feeds into larger ML models that make holistic predictions.

Bayesian filtering reads the words on your cargo manifest. Machine learning evaluates your entire voyage history, crew behavior, and trade relationships.