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: Outlyingness of observations in X according to the fitted model.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_features)
  
  Returns
  -------
  is_outliers : array, shape = (n_samples, ), dtype = bool
      For each observations, tells whether or not it should be considered
      as an outlier according to the fitted model.
  
  threshold : float,
      The values of the less outlying point's decision function.
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code: def predict(self, X):
      """Outlyingness of observations in X according to the fitted model.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_features)
  
      Returns
      -------
      is_outliers : array, shape = (n_samples, ), dtype = bool
          For each observations, tells whether or not it should be considered
          as an outlier according to the fitted model.
  
      threshold : float,
          The values of the less outlying point's decision function.
  
      """
      check_is_fitted(self, "threshold_")
      is_inlier = -np.ones(X.shape[0], dtype=int)
      if self.contamination is not None:
          values = self.decision_function(X, raw_values=True)
          is_inlier[values <= self.threshold_] = 1
      else:
          raise NotImplementedError(
              "You must provide a contamination rate."
          )
  
      return is_inlier
  
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 3 for calling the OutlierDetectionMixin obj's underlying member method code with example usage: obj.predict(X) and returns: is_inlier