sklearn.covariance

Methods and algorithms to robustly estimate covariance.

They estimate the covariance of features at given sets of points, as well as the precision matrix defined as the inverse of the covariance. Covariance estimation is closely related to the theory of Gaussian graphical models.

User guide. See the Covariance estimation section for further details.

EllipticEnvelope	An object for detecting outliers in a Gaussian distributed dataset.
EmpiricalCovariance	Maximum likelihood covariance estimator.
GraphicalLasso	Sparse inverse covariance estimation with an l1-penalized estimator.
GraphicalLassoCV	Sparse inverse covariance w/ cross-validated choice of the l1 penalty.
LedoitWolf	LedoitWolf Estimator.
MinCovDet	Minimum Covariance Determinant (MCD): robust estimator of covariance.
OAS	Oracle Approximating Shrinkage Estimator.
ShrunkCovariance	Covariance estimator with shrinkage.
empirical_covariance	Compute the Maximum likelihood covariance estimator.
graphical_lasso	L1-penalized covariance estimator.
ledoit_wolf	Estimate the shrunk Ledoit-Wolf covariance matrix.
ledoit_wolf_shrinkage	Estimate the shrunk Ledoit-Wolf covariance matrix.
oas	Estimate covariance with the Oracle Approximating Shrinkage.
shrunk_covariance	Calculate covariance matrices shrunk on the diagonal.