class_docstr: An object for detecting outliers in a Gaussian distributed dataset.
  
  Read more in the :ref:`User Guide <outlier_detection>`.
  
  Parameters
  ----------
  store_precision : bool
      Specify if the estimated precision is stored.
  
  assume_centered : Boolean
      If True, the support of robust location and 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, 0 < support_fraction < 1
      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`.
  
  contamination : float, 0. < contamination < 0.5
      The amount of contamination of the data set, i.e. the proportion
      of outliers in the data set.
  
  Attributes
  ----------
  location_ : array-like, shape (n_features,)
      Estimated robust location
  
  covariance_ : array-like, shape (n_features, n_features)
      Estimated robust covariance matrix
  
  precision_ : array-like, shape (n_features, n_features)
      Estimated pseudo inverse matrix.
      (stored only if store_precision is True)
  
  support_ : array-like, shape (n_samples,)
      A mask of the observations that have been used to compute the
      robust estimates of location and shape.
  
  See Also
  --------
  EmpiricalCovariance, MinCovDet
  
  Notes
  -----
  Outlier detection from covariance estimation may break or not
  perform well in high-dimensional settings. In particular, one will
  always take care to work with ``n_samples > n_features ** 2``.
  
  References
  ----------
  ..  [1] Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the minimum
      covariance determinant estimator" Technometrics 41(3), 212 (1999)
code: def fit(self, X, y=None):
      MinCovDet.fit(self, X)
      self.threshold_ = sp.stats.scoreatpercentile(
          self.dist_, 100.0 * (1.0 - self.contamination)
      )
      return self
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/covariance/outlier_detection.py
imports: import numpy
  import scipy
  from .None import MinCovDet
  from ..base import ClassifierMixin
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 2 for calling the EllipticEnvelope obj's underlying member method code with example usage: obj.fit(X, y) and returns: self