Want to understand how the best models in Numerai crush the competition every era? 🧵 Cue: Feature Exposure Feature Exposure is a diagnostic metric measuring how much your model depends on specific features. High exposure creates regime-dependent performance and should generally be reduced. Feature Neutralization is the technique for reducing exposure by removing linear feature relationships from predictions. It: 1) Uses pseudoinverse projection to residualize predictions 2) Preserves higher-order interactions while removing direct effects 3) Can be tuned via proportion parameter and feature selection The Core Trade-off: Lower exposure = more consistent performance but potentially lower correlation. The goal is finding your optimal balance. Best Practice: Selectively neutralize to the riskiest features with a tuned proportion parameter, rather than fully neutralizing to all features. --- This is the TLDR of feature exposures. Liked what you read? Let's strike a deal. > You RT > I get 100 retweets > I publish the full write-up below this