Last updated
Last updated
Feature neutral correlation (FNC) is the correlation of a model with the target, after its predictions have been neutralized to Numerai's features.
Since features are known to be inconsistent on their own, models with too much linear exposure to features are expected to perform poorly. By neutralizing this linear exposure to features, FNC isolates the predictive performance of the model that isn't just from the feature exposure.
To calculate a user's FNC for a given round we
Normalize the predictions in their submission
Neutralize their submission to Numerai's features for that round
Calculate the Spearman rank-order correlation of their neutralized submission to the target
The current version of FNC shown on the website is called FNCv3
which is neutral to the "medium" subset of features in the V3 data.
Read more about feature neutralization and feature exposure .