class_docstr: Wrap scikitlearn regressors.
  
  Parameters
  ----------
  strategy : str, default = "LightGBM"
      The choice for the regressor.
      Available strategies = {"LightGBM", "RandomForest", "ExtraTrees",
      "Tree", "Bagging", "AdaBoost" or "Linear"}
  
  **params : default = None
      Parameters of the corresponding regressor.
      Examples : n_estimators, max_depth...
class_docstr_tok: ["Wrap","scikitlearn","regressors",".","Parameters","--","--","--","--","--","strategy",":","str",",","default","=","``","LightGBM","''","The","choice","for","the","regressor",".","Available","strategies","=","{","``","LightGBM","''",",","``","RandomForest","''",",","``","ExtraTrees","''",",","``","Tree","''",",","``","Bagging","''",",","``","AdaBoost","''","or","``","Linear","''","}","*","*","params",":","default","=","None","Parameters","of","the","corresponding","regressor",".","Examples",":","n_estimators",",","max_depth","..."]
docstr: Init Regressor object where user can pass a strategy.
docstr_tok: ["Init","Regressor","object","where","user","can","pass","a","strategy","."]
code: def __init__(self, **params):
      """Init Regressor object where user can pass a strategy."""
      if "strategy" in params:
          self.__strategy = params["strategy"]
      else:
          self.__strategy = "LightGBM"
  
      self.__regress_params = {}
  
      self.__regressor = None
      self.__set_regressor(self.__strategy)
      self.__col = None
  
      self.set_params(**params)
      self.__fitOK = False
  
file_path: MLBox/mlbox/model/regression/regressor.py
imports: import warnings
  from copy import copy
  import numpy
  import pandas
  from sklearn.ensemble import AdaBoostRegressor, BaggingRegressor, ExtraTreesRegressor, RandomForestRegressor
  from sklearn.linear_model import Ridge
  from sklearn.tree import DecisionTreeRegressor
  from lightgbm import LGBMRegressor
label_desc: Use image node_id 1 to create a new Regressor object  with example: obj = Regressor()