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``.
code: def __init__(self, contamination=0.1):
      self.contamination = contamination
  
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 1 to create a new OutlierDetectionMixin object from inherited base classes: object with example: obj = OutlierDetectionMixin(contamination)