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.
class_docstr_tok: ["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","."]
docstr: Init method for class Categorical_encoder().
docstr_tok: ["Init","method","for","class","Categorical_encoder","(",")","."]
code: def __init__(self, strategy="label_encoding", verbose=False):
      """Init method for class Categorical_encoder()."""
      self.strategy = strategy
      self.verbose = verbose
      self.__Lcat = []
      self.__Lnum = []
      self.__Enc = dict()
      self.__K = dict()
      self.__weights = None
      self.__fitOK = False
  
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 1 to create a new Categorical_encoder object  with example: obj = Categorical_encoder(strategy, verbose)