class_docstr: Median Absolute Deviation: for measuring the distances between
  data points and the median in terms of median distance.
  See :cite:`iglewicz1993detect` for details.
  
  Parameters
  ----------
  threshold : float, optional (default=3.5)
     The modified z-score to use as a threshold. Observations with
     a modified z-score (based on the median absolute deviation) greater
     than this value will be classified as outliers.
  
  Attributes
  ----------
  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.
  
  threshold_ : float
     The modified z-score to use as a threshold. Observations with
     a modified z-score (based on the median absolute deviation) greater
     than this value will be classified as outliers.
  
  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: This overrides PyOD base class function in order to use the
  proper `threshold_` which is quite different in the base class.
  Internal function to calculate key attributes:
  - labels_: binary labels of training data.
  - _mu: mean of decision scores.
  - _sigma: standard deviation of decision scores.
  
  Returns
  -------
  self
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code: def _process_decision_scores(self):
      """This overrides PyOD base class function in order to use the
      proper `threshold_` which is quite different in the base class.
      Internal function to calculate key attributes:
      - labels_: binary labels of training data.
      - _mu: mean of decision scores.
      - _sigma: standard deviation of decision scores.
  
      Returns
      -------
      self
      """
      self.labels_ = (
          (self.decision_scores_ > self.threshold).astype("int").ravel()
      )
  
      # calculate for predict_proba()
      self._mu = np.nanmean(self.decision_scores_)
      self._sigma = np.nanstd(self.decision_scores_)
  
      return self
  
file_path: pyod/pyod/models/mad.py
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  from sklearn.utils import check_array
  from .base import BaseDetector
label_desc: Use image node_id 5 for calling the MAD obj's underlying member method code with example usage: obj._process_decision_scores() and returns: self