Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.covariance`.ledoit_wolf_shrinkage

sklearn.covariance.ledoit_wolf_shrinkage(X, assume_centered=False, block_size=1000)

  Estimate the shrunk Ledoit-Wolf covariance matrix.

  Read more in the User Guide.

  Parameters:
    X array-like of shape (n_samples, n_features)

      Data from which to compute the Ledoit-Wolf shrunk covariance shrinkage.

    assume_centered bool, default=False

      If True, data will not be centered before computation. Useful to work with data whose mean is significantly equal to zero but is not exactly zero. If False, data will be centered before computation.

    block_size int, default=1000

      Size of blocks into which the covariance matrix will be split.

  Returns:
    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