sklearn.covariance.ledoit_wolf¶
- sklearn.covariance.ledoit_wolf(X, assume_centered=False, block_size=1000)[source]¶
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, default=False
If True, data are not centered before computation. Useful to work with data whose mean is significantly equal to zero but is not exactly zero. If False, data are centered before computation.
block_size : int, default=1000
Size of the blocks into which the covariance matrix will be split. This is purely a memory optimization and does not affect results.
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 regularized (shrunk) covariance is:
- (1 - shrinkage)*cov
- shrinkage * mu * np.identity(n_features)
where mu = trace(cov) / n_features
