6.9.3. scikits.learn.covariance.LedoitWolf¶
- class scikits.learn.covariance.LedoitWolf(store_covariance=True)¶
LedoitWolf Estimator
Parameters : store_covariance : bool
Specify if the estimated covariance is stored
Notes
The regularised covariance is:
(1 - shrinkage)*cov + shrinkage*mu*np.identity(n_features)
where mu = trace(cov) / n_features
Reference : “A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices”, Ledoit and Wolf, Journal of Multivariate Analysis, Volume 88, Issue 2, February 2004, pages 365-411.
Attributes
covariance_ 2D ndarray, shape (n_features, n_features) Estimated covariance matrix (stored only is store_covariance is True) precision_ 2D ndarray, shape (n_features, n_features) Estimated precision matrix shrinkage_ float Scalar used to regularize the precision matrix estimation Methods
fit(X) log_likelihood(test_cov) score(X_test) - __init__(store_covariance=True)¶