class_docstr: Set of methods for outliers detection with covariance estimators.
  
  Parameters
  ----------
  contamination : float, 0. < contamination < 0.5
      The amount of contamination of the data set, i.e. the proportion
      of outliers in the data set.
  
  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``.
docstr: Compute the decision function of the given observations.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
  
  raw_values : bool
      Whether or not to consider raw Mahalanobis distances as the
      decision function. Must be False (default) for compatibility
      with the others outlier detection tools.
  
  Returns
  -------
  decision : array-like, shape (n_samples, )
      The values of the decision function for each observations.
      It is equal to the Mahalanobis distances if `raw_values`
      is True. By default (``raw_values=True``), it is equal
      to the cubic root of the shifted Mahalanobis distances.
      In that case, the threshold for being an outlier is 0, which
      ensures a compatibility with other outlier detection tools
      such as the One-Class SVM.
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code: def decision_function(self, X, raw_values=False):
      """Compute the decision function of the given observations.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
  
      raw_values : bool
          Whether or not to consider raw Mahalanobis distances as the
          decision function. Must be False (default) for compatibility
          with the others outlier detection tools.
  
      Returns
      -------
      decision : array-like, shape (n_samples, )
          The values of the decision function for each observations.
          It is equal to the Mahalanobis distances if `raw_values`
          is True. By default (``raw_values=True``), it is equal
          to the cubic root of the shifted Mahalanobis distances.
          In that case, the threshold for being an outlier is 0, which
          ensures a compatibility with other outlier detection tools
          such as the One-Class SVM.
  
      """
      check_is_fitted(self, "threshold_")
      mahal_dist = self.mahalanobis(X)
      if raw_values:
          decision = mahal_dist
      else:
          check_is_fitted(self, "threshold_")
          transformed_mahal_dist = mahal_dist**0.33
          decision = self.threshold_**0.33 - transformed_mahal_dist
  
      return decision
  
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 OutlierDetectionMixin obj's underlying member method code with example usage: obj.decision_function(X, raw_values) and returns: decision