sklearn.covariance Module

An sklearn.covariance Module is an sklearn module that contains a collection of Covariance Estimators.

• Context:
  • It can (often) reference a sklearn.covariance system.
    • sklearn.covariance.Covariance_Name(self, arguments) or simply sklearn.metrics.Metric_Name()

      where Covariance_Name is the name of the selected Covariance Estimator.

• Example(s)
• Counter-Example(s):
  • sklearn.manifold, a collection of Manifold Learning Systems.
  • sklearn.tree, a collection of Decision Tree Learning Systems.
  • sklearn.ensemble, a collection of Decision Tree Ensemble Learning Systems.
  • sklearn.metrics, a collection of Metrics Subroutines.
  • sklearn.cluster.bicluster, a collection of Spectral Biclustering Algorithms.
  • sklearn.linear_model, a collection of Linear Model Regression Systems.
  • sklearn.neighbors, a collection of K Nearest Neighbors Algorithms .
• See: Variance, Covariance, Population Parameter, Correlation.

---

---

References

2017A

• (Scikit Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/classes.html#module-sklearn.covariance Retrieved:2017-11-12
  • QUOTE: The sklearn.covariance module includes methods and algorithms to robustly estimate the covariance of features given a set of points. The precision matrix defined as the inverse of the covariance is also estimated. Covariance estimation is closely related to the theory of Gaussian Graphical Models.

    User guide: See the [Covariance estimation] section for further details.

    • covariance.EmpiricalCovariance([…]), Maximum likelihood covariance estimator
    • covariance.EllipticEnvelope([…]), An object for detecting outliers in a Gaussian distributed dataset.
    • covariance.GraphLasso([alpha, mode, tol, …]), Sparse inverse covariance estimation with an l1-penalized estimator.
    • covariance.GraphLassoCV([alphas, …]), Sparse inverse covariance w/ cross-validated choice of the l1 penalty
    • covariance.LedoitWolf([store_precision, …]), LedoitWolf Estimator
    • covariance.MinCovDet([store_precision, …]), Minimum Covariance Determinant (MCD): robust estimator of covariance.
    • covariance.OAS([store_precision, …]), Oracle Approximating Shrinkage Estimator
    • covariance.ShrunkCovariance([…]), Covariance estimator with shrinkage
    • covariance.empirical_covariance(X[, …]), Computes the Maximum likelihood covariance estimator
    • covariance.graph_lasso(emp_cov, alpha[, …]), l1-penalized covariance estimator
    • covariance.ledoit_wolf(X[, assume_centered, …]), Estimates the shrunk Ledoit-Wolf covariance matrix.
    • covariance.oas(X[, assume_centered]), Estimate covariance with the Oracle Approximating Shrinkage algorithm.
    • covariance.shrunk_covariance(emp_cov[, …]), Calculates a covariance matrix shrunk on the diagonal

2017B

• (Scikit Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/covariance.html Retrieved:2017-11-12
  • QUOTE: Many statistical problems require at some point the estimation of a population’s covariance matrix, which can be seen as an estimation of data set scatter plot shape. Most of the time, such an estimation has to be done on a sample whose properties (size, structure, homogeneity) has a large influence on the estimation’s quality. The sklearn.covariance package aims at providing tools affording an accurate estimation of a population’s covariance matrix under various settings.

    We assume that the observations are independent and identically distributed (i.i.d.).

---