class_docstr: Minimum Covariance Determinant (MCD): robust estimator of covariance.
  
  The Minimum Covariance Determinant covariance estimator is to be applied
  on Gaussian-distributed data, but could still be relevant on data
  drawn from a unimodal, symmetric distribution. It is not meant to be used
  with multi-modal 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 multi-modal
  datasets.
  
  Read more in the :ref:`User Guide <robust_covariance>`.
  
  Parameters
  ----------
  store_precision : bool, default=True
      Specify if the estimated precision is stored.
  
  assume_centered : bool, default=False
      If True, the support of the robust location and the covariance
      estimates is computed, and a covariance estimate is recomputed from
      it, without centering the data.
      Useful to work with data whose mean is significantly equal to
      zero but is not exactly zero.
      If False, the robust location and covariance are directly computed
      with the FastMCD algorithm without additional treatment.
  
  support_fraction : float, default=None
      The proportion of points to be included in the support of the raw
      MCD estimate. Default is None, which implies that the minimum
      value of support_fraction will be used within the algorithm:
      `(n_sample + n_features + 1) / 2`. The parameter must be in the range
      (0, 1].
  
  random_state : int, RandomState instance or None, default=None
      Determines the pseudo random number generator for shuffling the data.
      Pass an int for reproducible results across multiple function calls.
      See :term:`Glossary <random_state>`.
  
  Attributes
  ----------
  raw_location_ : ndarray of shape (n_features,)
      The raw robust estimated location before correction and re-weighting.
  
  raw_covariance_ : ndarray of shape (n_features, n_features)
      The raw robust estimated covariance before correction and re-weighting.
  
  raw_support_ : ndarray of shape (n_samples,)
      A mask of the observations that have been used to compute
      the raw robust estimates of location and shape, before correction
      and re-weighting.
  
  location_ : ndarray of shape (n_features,)
      Estimated robust location.
  
  covariance_ : ndarray of shape (n_features, n_features)
      Estimated robust covariance matrix.
  
  precision_ : ndarray of shape (n_features, n_features)
      Estimated pseudo inverse matrix.
      (stored only if store_precision is True)
  
  support_ : ndarray of shape (n_samples,)
      A mask of the observations that have been used to compute
      the robust estimates of location and shape.
  
  dist_ : ndarray of shape (n_samples,)
      Mahalanobis distances of the training set (on which :meth:`fit` is
      called) observations.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  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 with cross-validated
      choice of the l1 penalty.
  LedoitWolf : LedoitWolf Estimator.
  OAS : Oracle Approximating Shrinkage Estimator.
  ShrunkCovariance : Covariance estimator with shrinkage.
  
  References
  ----------
  
  .. [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, 1385-1400
  
  Examples
  --------
  >>> import numpy as np
  >>> from sklearn.covariance import MinCovDet
  >>> from sklearn.datasets import make_gaussian_quantiles
  >>> real_cov = np.array([[.8, .3],
  ...                      [.3, .4]])
  >>> rng = np.random.RandomState(0)
  >>> X = rng.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...])
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code: def __init__(
      self,
      *,
      store_precision=True,
      assume_centered=False,
      support_fraction=None,
      random_state=None,
  ):
      self.store_precision = store_precision
      self.assume_centered = assume_centered
      self.support_fraction = support_fraction
      self.random_state = random_state
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/covariance/_robust_covariance.py
imports: import warnings
  from numbers import Integral, Real
  import numpy
  from scipy import linalg
  from scipy.stats import chi2
  from ..base import _fit_context
  from ..utils import check_array, check_random_state
  from ..utils._param_validation import Interval
  from ..utils.extmath import fast_logdet
  from ._empirical_covariance import EmpiricalCovariance, empirical_covariance
label_desc: Use image node_id 1 to create a new MinCovDet object from inherited base classes: EmpiricalCovariance with example: obj = MinCovDet()