class_docstr: Numeric attribute observer for classification tasks that is based on
  Gaussian estimators.
  
  The distribution of each class is approximated using a Gaussian distribution. Hence,
  the probability density function can be easily calculated.
  
  
  Parameters
  ----------
  n_splits
      The number of partitions to consider when querying for split candidates.
class_docstr_tok: ["Numeric","attribute","observer","for","classification","tasks","that","is","based","on","Gaussian","estimators",".","The","distribution","of","each","class","is","approximated","using","a","Gaussian","distribution",".","Hence",",","the","probability","density","function","can","be","easily","calculated",".","Parameters","--","--","--","--","--","n_splits","The","number","of","partitions","to","consider","when","querying","for","split","candidates","."]
code: def _split_point_suggestions(self):
      suggested_split_values = []
      min_value = math.inf
      max_value = -math.inf
      for k, estimator in self._att_dist_per_class.items():
          if self._min_per_class[k] < min_value:
              min_value = self._min_per_class[k]
          if self._max_per_class[k] > max_value:
              max_value = self._max_per_class[k]
      if min_value < math.inf:
          bin_size = max_value - min_value
          bin_size /= self.n_splits + 1.0
          for i in range(self.n_splits):
              split_value = min_value + (bin_size * (i + 1))
              if min_value < split_value < max_value:
                  suggested_split_values.append(split_value)
      return suggested_split_values
  
file_path: river/river/tree/splitter/gaussian_splitter.py
imports: from __future__ import annotations
  import math
  from river.base.typing import ClfTarget
  from river.proba import Gaussian
  from ..utils import BranchFactory
  from .base import Splitter
label_desc: Use image node_id 5 for calling the GaussianSplitter obj's underlying member method code with example usage: obj._split_point_suggestions() and returns: suggested_split_values