class_docstr: Detecting outliers in a Gaussian distributed dataset using
  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.
  
  First fit a minimum covariance determinant model and then compute the
  Mahalanobis distance as the outlier degree of the data
  
  See :cite:`rousseeuw1999fast,hardin2004outlier` for details.
  
  Parameters
  ----------
  contamination : float in (0., 0.5), optional (default=0.1)
      The amount of contamination of the data set,
      i.e. the proportion of outliers in the data set. Used when fitting to
      define the threshold on the decision function.
  
  store_precision : bool
      Specify if the estimated precision is stored.
  
  assume_centered : bool
      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, 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
  
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  
  Attributes
  ----------
  raw_location_ : array-like, shape (n_features,)
      The raw robust estimated location before correction and re-weighting.
  
  raw_covariance_ : array-like, shape (n_features, n_features)
      The raw robust estimated covariance before correction and re-weighting.
  
  raw_support_ : array-like, 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_ : 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.
  
  decision_scores_ : numpy array of shape (n_samples,)
      The outlier scores of the training data.
      The higher, the more abnormal. Outliers tend to have higher
      scores. This value is available once the detector is
      fitted. Mahalanobis distances of the training set (on which
      `:meth:`fit` is called) observations.
  
  threshold_ : float
      The threshold is based on ``contamination``. It is the
      ``n_samples * contamination`` most abnormal samples in
      ``decision_scores_``. The threshold is calculated for generating
      binary outlier labels.
  
  labels_ : int, either 0 or 1
      The binary labels of the training data. 0 stands for inliers
      and 1 for outliers/anomalies. It is generated by applying
      ``threshold_`` on ``decision_scores_``.
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docstr: A mask of the observations that have been used to compute
  the robust estimates of location and shape.
  
  Decorator for scikit-learn MinCovDet attributes.
docstr_tok: ["A","mask","of","the","observations","that","have","been","used","to","compute","the","robust","estimates","of","location","and","shape",".","Decorator","for","scikit-learn","MinCovDet","attributes","."]
code: def support_(self):
      """A mask of the observations that have been used to compute
      the robust estimates of location and shape.
  
      Decorator for scikit-learn MinCovDet attributes.
      """
      return self.detector_.support_
  
file_path: pyod/pyod/models/mcd.py
imports: from __future__ import division
  from __future__ import print_function
  from sklearn.covariance import MinCovDet
  from sklearn.utils.validation import check_array
  from sklearn.utils.validation import check_is_fitted
  from .base import BaseDetector
label_desc: Use image node_id 10 for calling the MCD obj's underlying member method code with example usage: obj.support_() and returns: self