class_docstr: Removes low-variance features.
  
  Parameters
  ----------
  threshold
      Only features with a variance above the threshold will be kept.
  min_samples
      The minimum number of samples required to perform selection.
  
  Attributes
  ----------
  variances : dict
      The variance of each feature.
  
  Examples
  --------
  
  >>> from river import feature_selection
  >>> from river import stream
  
  >>> X = [
  ...     [0, 2, 0, 3],
  ...     [0, 1, 4, 3],
  ...     [0, 1, 1, 3]
  ... ]
  
  >>> selector = feature_selection.VarianceThreshold()
  
  >>> for x, _ in stream.iter_array(X):
  ...     selector.learn_one(x)
  ...     print(selector.transform_one(x))
  {0: 0, 1: 2, 2: 0, 3: 3}
  {1: 1, 2: 4}
  {1: 1, 2: 1}
code: def transform_one(self, x):
      return {i: xi for i, xi in x.items() if self.check_feature(i)}
  
file_path: river/river/feature_selection/variance.py
imports: from __future__ import annotations
  import collections
  from river import base, stats
label_desc: Use image node_id 4 for calling the VarianceThreshold obj's underlying member method code with example usage: obj.transform_one(x) and returns: unknown