class_docstr: Abstract class - Build a class that inherits from this class to implement
  a custom discretizer.
  Method bins() is to be redefined in the child class, as it is the actual
  custom part of the discretizer.
class_docstr_tok: ["Abstract","class","-","Build","a","class","that","inherits","from","this","class","to","implement","a","custom","discretizer",".","Method","bins","(",")","is","to","be","redefined","in","the","child","class",",","as","it","is","the","actual","custom","part","of","the","discretizer","."]
docstr: To be overridden
  Returns for each feature to discretize the boundaries
  that form each bin of the discretizer
docstr_tok: ["To","be","overridden","Returns","for","each","feature","to","discretize","the","boundaries","that","form","each","bin","of","the","discretizer"]
code: def bins(self, data, labels):
      """
      To be overridden
      Returns for each feature to discretize the boundaries
      that form each bin of the discretizer
      """
      raise NotImplementedError("Must override bins() method")
  
imports: import numpy
  import sklearn
  import sklearn.tree
  import scipy
  from sklearn.utils import check_random_state
  from abc import ABCMeta, abstractmethod
label_desc: Use image node_id 2 for calling the BaseDiscretizer obj's underlying member method code with example usage: obj.bins(data, labels) without return types