class_docstr: Feature selector that removes all low-variance features.
  
  This feature selection algorithm looks only at the features (X), not the
  desired outputs (y), and can thus be used for unsupervised learning.
  
  Read more in the :ref:`User Guide <variance_threshold>`.
  
  Parameters
  ----------
  threshold : float, default=0
      Features with a training-set variance lower than this threshold will
      be removed. The default is to keep all features with non-zero variance,
      i.e. remove the features that have the same value in all samples.
  
  Attributes
  ----------
  variances_ : array, shape (n_features,)
      Variances of individual features.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  SelectFromModel: Meta-transformer for selecting features based on
      importance weights.
  SelectPercentile : Select features according to a percentile of the highest
      scores.
  SequentialFeatureSelector : Transformer that performs Sequential Feature
      Selection.
  
  Notes
  -----
  Allows NaN in the input.
  Raises ValueError if no feature in X meets the variance threshold.
  
  Examples
  --------
  The following dataset has integer features, two of which are the same
  in every sample. These are removed with the default setting for threshold::
  
      >>> from sklearn.feature_selection import VarianceThreshold
      >>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
      >>> selector = VarianceThreshold()
      >>> selector.fit_transform(X)
      array([[2, 0],
             [1, 4],
             [1, 1]])
class_docstr_tok: ["Feature","selector","that","removes","all","low-variance","features",".","This","feature","selection","algorithm","looks","only","at","the","features","(","X",")",",","not","the","desired","outputs","(","y",")",",","and","can","thus","be","used","for","unsupervised","learning",".","Read","more","in","the",":","ref",":","`","User","Guide","<","variance_threshold",">","`",".","Parameters","--","--","--","--","--","threshold",":","float",",","default=0","Features","with","a","training-set","variance","lower","than","this","threshold","will","be","removed",".","The","default","is","to","keep","all","features","with","non-zero","variance",",","i.e",".","remove","the","features","that","have","the","same","value","in","all","samples",".","Attributes","--","--","--","--","--","variances_",":","array",",","shape","(","n_features",",",")","Variances","of","individual","features",".","n_features_in_",":","int","Number","of","features","seen","during",":","term",":","`","fit","`",".","..","versionadded",":",":","0.24","feature_names_in_",":","ndarray","of","shape","(","`","n_features_in_","`",",",")","Names","of","features","seen","during",":","term",":","`","fit","`",".","Defined","only","when","`","X","`","has","feature","names","that","are","all","strings",".","..","versionadded",":",":","1.0","See","Also","--","--","--","--","SelectFromModel",":","Meta-transformer","for","selecting","features","based","on","importance","weights",".","SelectPercentile",":","Select","features","according","to","a","percentile","of","the","highest","scores",".","SequentialFeatureSelector",":","Transformer","that","performs","Sequential","Feature","Selection",".","Notes","--","--","-","Allows","NaN","in","the","input",".","Raises","ValueError","if","no","feature","in","X","meets","the","variance","threshold",".","Examples","--","--","--","--","The","following","dataset","has","integer","features",",","two","of","which","are","the","same","in","every","sample",".","These","are","removed","with","the","default","setting","for","threshold",":",":",">",">",">","from","sklearn.feature_selection","import","VarianceThreshold",">",">",">","X","=","[","[","0",",","2",",","0",",","3","]",",","[","0",",","1",",","4",",","3","]",",","[","0",",","1",",","1",",","3","]","]",">",">",">","selector","=","VarianceThreshold","(",")",">",">",">","selector.fit_transform","(","X",")","array","(","[","[","2",",","0","]",",","[","1",",","4","]",",","[","1",",","1","]","]",")"]
code: def _get_support_mask(self):
      check_is_fitted(self)
  
      return self.variances_ > self.threshold
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/feature_selection/_variance_threshold.py
imports: from numbers import Real
  import numpy
  from ..base import BaseEstimator, _fit_context
  from ..utils._param_validation import Interval
  from ..utils.sparsefuncs import mean_variance_axis, min_max_axis
  from ..utils.validation import check_is_fitted
  from ._base import SelectorMixin
label_desc: Use image node_id 3 for calling the VarianceThreshold obj's underlying member method code with example usage: obj._get_support_mask() and returns: unknown