class sklearn.covariance.MinCovDet(store_precision=True, assume_centered=False, support_fraction=None, random_state=None)
[source]
Minimum Covariance Determinant (MCD): robust estimator of covariance.
The Minimum Covariance Determinant covariance estimator is to be applied on Gaussiandistributed data, but could still be relevant on data drawn from a unimodal, symmetric distribution. It is not meant to be used with multimodal data (the algorithm used to fit a MinCovDet object is likely to fail in such a case). One should consider projection pursuit methods to deal with multimodal datasets.
Read more in the User Guide.
Parameters: 


Attributes: 

[Rouseeuw1984]  P. J. Rousseeuw. Least median of squares regression. J. Am Stat Ass, 79:871, 1984. 
[Rousseeuw]  A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS 
[ButlerDavies]  R. W. Butler, P. L. Davies and M. Jhun, Asymptotics For The Minimum Covariance Determinant Estimator, The Annals of Statistics, 1993, Vol. 21, No. 3, 13851400 
>>> import numpy as np >>> from sklearn.covariance import MinCovDet >>> from sklearn.datasets import make_gaussian_quantiles >>> real_cov = np.array([[.8, .3], ... [.3, .4]]) >>> np.random.seed(0) >>> X = np.random.multivariate_normal(mean=[0, 0], ... cov=real_cov, ... size=500) >>> cov = MinCovDet(random_state=0).fit(X) >>> cov.covariance_ array([[0.7411..., 0.2535...], [0.2535..., 0.3053...]]) >>> cov.location_ array([0.0813... , 0.0427...])
correct_covariance (data)  Apply a correction to raw Minimum Covariance Determinant estimates. 
error_norm (comp_cov[, norm, scaling, squared])  Computes the Mean Squared Error between two covariance estimators. 
fit (X[, y])  Fits a Minimum Covariance Determinant with the FastMCD algorithm. 
get_params ([deep])  Get parameters for this estimator. 
get_precision ()  Getter for the precision matrix. 
mahalanobis (X)  Computes the squared Mahalanobis distances of given observations. 
reweight_covariance (data)  Reweight raw Minimum Covariance Determinant estimates. 
score (X_test[, y])  Computes the loglikelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix. 
set_params (**params)  Set the parameters of this estimator. 
__init__(store_precision=True, assume_centered=False, support_fraction=None, random_state=None)
[source]
correct_covariance(data)
[source]
Apply a correction to raw Minimum Covariance Determinant estimates.
Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in [RVD].
Parameters: 


Returns: 

[RVD] 
(1, 2) A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS

error_norm(comp_cov, norm=’frobenius’, scaling=True, squared=True)
[source]
Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).
Parameters: 


Returns: 

fit(X, y=None)
[source]
Fits a Minimum Covariance Determinant with the FastMCD algorithm.
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

get_precision()
[source]
Getter for the precision matrix.
Returns: 


mahalanobis(X)
[source]
Computes the squared Mahalanobis distances of given observations.
Parameters: 


Returns: 

reweight_covariance(data)
[source]
Reweight raw Minimum Covariance Determinant estimates.
Reweight observations using Rousseeuw’s method (equivalent to deleting outlying observations from the data set before computing location and covariance estimates) described in [RVDriessen].
Parameters: 


Returns: 

[RVDriessen] 
(1, 2) A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS

score(X_test, y=None)
[source]
Computes the loglikelihood of a Gaussian data set with self.covariance_
as an estimator of its covariance matrix.
Parameters: 


Returns: 

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


sklearn.covariance.MinCovDet
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.covariance.MinCovDet.html