class_docstr: Base class for advanced discretisation methods
docstr: Discretise the features in `feat_names` in such a way that maximises the prediction of `target`.
  
  Args:
      feat_names (List[str]): List of feature names to be discretised.
      target (str): Name of the target variable - the node that adjusts how `feat_names` will be discretised
      dataframe: The full dataset prior to discretisation.
      target_continuous (bool): Boolean indicates if target variable is continuous
  Raises:
      NotImplementedError: AbstractSupervisedDiscretiserMethod should not be called directly
docstr_tok: ["Discretise","the","features","in","`","feat_names","`","in","such","a","way","that","maximises","the","prediction","of","`","target","`",".","Args",":","feat_names","(","List","[","str","]",")",":","List","of","feature","names","to","be","discretised",".","target","(","str",")",":","Name","of","the","target","variable","-","the","node","that","adjusts","how","`","feat_names","`","will","be","discretised","dataframe",":","The","full","dataset","prior","to","discretisation",".","target_continuous","(","bool",")",":","Boolean","indicates","if","target","variable","is","continuous","Raises",":","NotImplementedError",":","AbstractSupervisedDiscretiserMethod","should","not","be","called","directly"]
code: def fit(
      self,
      feat_names: List[str],
      target: str,
      dataframe: pd.DataFrame,
      target_continuous: bool,
  ):
      """
      Discretise the features in `feat_names` in such a way that maximises the prediction of `target`.
  
      Args:
          feat_names (List[str]): List of feature names to be discretised.
          target (str): Name of the target variable - the node that adjusts how `feat_names` will be discretised
          dataframe: The full dataset prior to discretisation.
          target_continuous (bool): Boolean indicates if target variable is continuous
      Raises:
          NotImplementedError: AbstractSupervisedDiscretiserMethod should not be called directly
  
      """
      raise NotImplementedError("The method is not implemented")
  
file_path: causalnex/causalnex/discretiser/abstract_discretiser.py
imports: import logging
  from abc import ABC, abstractmethod
  from typing import List
  import numpy
  import pandas
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 2 for calling the AbstractSupervisedDiscretiserMethod obj's underlying member method code with example usage: obj.fit(feat_names, target, dataframe, target_continuous) without return types