Feature neutral correlation (FNC) is the correlation of a model with the target, after its predictions have been neutralized to all of Numerai’s features.
A model that is overly reliant on a small set of features will have a low FNC, but might still have a high correlation in the short term. However, it is also more likely to burn significantly in the long term.
A model that uses a diverse set of features and is still correlated with the targets will have a high FNC, and is more likely to have consistent performance over the long term.
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
defcalculate_fnc(sub, targets, features):
# Normalize submission
sub =(sub.rank(method="first").values -0.5)/len(sub)
# Neutralize submission to features
f = features.values
sub -= f.dot(np.linalg.pinv(f).dot(sub))
sub /= sub.std()
sub = pd.Series(np.squeeze(sub))# Convert np.ndarray to pd.Series
# FNC: Spearman rank-order correlation of neutralized submission to target