What is the effect of recency bias in machine learning filters?

Recency bias in filtering means recent behavior matters most:

Machine learning models often weight recent data more heavily. Your last few sends matter more than sends from six months ago.

• Implications:
• Recent problems can quickly damage placement, even with strong historical reputation.
• Recent improvements can lift placement faster than purely historical approaches would suggest.
• Consistency matters. Sustained good behavior compounds positively. Sustained poor behavior compounds negatively.
• Your recent voyages matter more than your distant past. Today's behavior shapes tomorrow's welcome.