class_docstr: Encodes categorical features.
  
  Several strategies are possible (supervised or not). Works for both
  classification and regression tasks.
  
  Parameters
  ----------
  strategy : str, default = "label_encoding"
      The strategy to encode categorical features.
      Available strategies = {"label_encoding", "dummification",
      "random_projection", entity_embedding"}
  verbose : bool, default = False
      Verbose mode. Useful for entity embedding strategy.
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docstr: Set param method for Categorical encoder.
  
  Set strategy parameters and verbose parameters
  
  Parameters
  ----------
  strategy : str, default = "label_encoding"
      The strategy to encode categorical features.
      Available strategies = {"label_encoding", "dummification",
      "random_projection", entity_embedding"}
  verbose : bool, default = False
      Verbose mode. Useful for entity embedding strategy.
docstr_tok: ["Set","param","method","for","Categorical","encoder",".","Set","strategy","parameters","and","verbose","parameters","Parameters","--","--","--","--","--","strategy",":","str",",","default","=","``","label_encoding","''","The","strategy","to","encode","categorical","features",".","Available","strategies","=","{","``","label_encoding","''",",","``","dummification","''",",","``","random_projection","''",",","entity_embedding","''","}","verbose",":","bool",",","default","=","False","Verbose","mode",".","Useful","for","entity","embedding","strategy","."]
code: def set_params(self, **params):
      """Set param method for Categorical encoder.
  
      Set strategy parameters and verbose parameters
  
      Parameters
      ----------
      strategy : str, default = "label_encoding"
          The strategy to encode categorical features.
          Available strategies = {"label_encoding", "dummification",
          "random_projection", entity_embedding"}
      verbose : bool, default = False
          Verbose mode. Useful for entity embedding strategy.
  
      """
      self.__fitOK = False
  
      for k, v in params.items():
          if k not in self.get_params():
              warnings.warn(
                  "Invalid parameter(s) for encoder "
                  "Categorical_encoder. Parameter(s) IGNORED. "
                  "Check the list of available parameters with "
                  "`encoder.get_params().keys()`"
              )
          else:
              setattr(self, k, v)
  
file_path: MLBox/mlbox/encoding/categorical_encoder.py
imports: import numpy
  import pandas
  import warnings
  import os
  from tensorflow.keras.layers import Dense, Reshape, Dropout, Embedding, concatenate, Input
  from tensorflow.keras.models import Model
label_desc: Use image node_id 3 for calling the Categorical_encoder obj's underlying member method code with example usage: obj.set_params() without return types