How do spam filters “learn”?

Spam filters learn through a combination of supervised learning, user feedback, and pattern recognition. The process is continuous and never stops.

Supervised learning involves training models on labeled datasets where messages are already classified as spam or legitimate. The model learns to recognize features that distinguish the two categories. Major providers maintain massive training datasets built from years of mail flow.

User feedback provides ongoing refinement. Every time someone clicks "Report Spam" or rescues a message from the junk folder, that action becomes a signal. Aggregated across millions of users, these signals reveal new spam patterns and correct false positives.

Pattern recognition detects anomalies in real time. If a sender suddenly changes behavior, if a new spam campaign emerges, or if a previously trusted domain starts sending suspicious content, the filter adapts.

The filter is a student that never graduates. It keeps learning because the curriculum keeps changing.