8.2.9. sklearn.covariance.ledoit_wolf¶
- sklearn.covariance.ledoit_wolf(X, assume_centered=False)¶
Estimates the shrunk Ledoit-Wolf covariance matrix.
Parameters : X: array-like, shape (n_samples, n_features) :
Data from which to compute the covariance estimate
assume_centered: Boolean :
If True, data are not centered before computation. Usefull to work with data whose mean is significantly equal to zero but is not exactly zero. If False, data are centered before computation.
Returns : shrunk_cov: array-like, shape (n_features, n_features) :
Shrunk covariance
shrinkage: float :
coefficient in the convex combination used for the computation of the shrunk estimate.
Notes
The regularised (shrunk) covariance is:
- (1 - shrinkage)*cov
- shrinkage * mu * np.identity(n_features)
where mu = trace(cov) / n_features