sklearn.covariance Module
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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 simplysklearn.metrics.Metric_Name()
where Covariance_Name is the name of the selected Covariance Estimator.
- It can (often) reference a sklearn.covariance system.
- Example(s)
sklearn.covariance.EmpiricalCovariance
, an Empirical Covariance based on a Maximum Likelihood Estimator.sklearn.covariance.empirical_covariance
, an Emperical Covariance based on a Maximum Likelihood Estimator.sklearn.covariance.EllipticEnvelope
, An object for detecting outliers in a Gaussian distributed dataset.sklearn.covariance.GraphLasso
, Sparse inverse covariance estimation based on l1-penalized estimator.sklearn.covariance.GraphLassoCV
, Sparse inverse covariance based on cross-validation and l1 penalty.sklearn.covariance.graph_lasso
, a l1-penalized covariance estimator.sklearn.covariance.LedoitWolf
, a LedoitWolf Covariance Estimator.sklearn.covariance.ledoit_wolf
, a Covariance Estimate based on a reducedLedoit-Wolf covariance matrix.sklearn.covariance.MinCovDet
, a Robust Covariance Estimator based on the Minimum Covariance Determinant (MCD).sklearn.covariance.OAS
, a Covariance Estimate based on Oracle Approximating Shrinkage Algorithm.sklearn.covariance.oas
, a Covariance Estimate based on Oracle Approximating Shrinkage algorithm.sklearn.covariance.ShrunkCovariance
, a Covariance Estimator based on a Shrinkage Algorithm.sklearn.covariance.shrunk_covariance
, a Covariance Estimator based on a Shrinkage Algorithm.- …
- 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.sklearn.neural_network
, a collection of Neural Network Systems.
- 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
- 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.
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.).
- 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.