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scikit-learn 1.3.2
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# `sklearn.covariance`.empirical_covariance

sklearn.covariance.empirical_covariance(X, *, assume_centered=False)

  Compute the Maximum likelihood covariance estimator.

  Parameters:
    X ndarray of shape (n_samples, n_features)

      Data from which to compute the covariance estimate.

    assume_centered bool, default=False

      If `True`, data will not be centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If `False`, data will be centered before computation.

  Returns:
    covariance ndarray of shape (n_features, n_features)

      Empirical covariance (Maximum Likelihood Estimator).

  Examples

      >>> from sklearn.covariance import empirical_covariance
      >>> X = [[1,1,1],[1,1,1],[1,1,1],
      ...      [0,0,0],[0,0,0],[0,0,0]]
      >>> empirical_covariance(X)
      array([[0.25, 0.25, 0.25],
             [0.25, 0.25, 0.25],
             [0.25, 0.25, 0.25]])

## Examples using `sklearn.covariance.empirical_covariance`

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood