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Feature Neutral Correlation (FNC)
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
def calculate_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
fnc = np.corrcoef(sub.rank(pct=True, method="first"), targets)[0, 1]
The current version of FNC shown on the website is called
FNCv3which is nuetral to the "medium" subset of features in the V3 data.