class_docstr: Wraps scikitlearn classifiers.
  
  Parameters
  ----------
  strategy : str, default = "LightGBM"
      The choice for the classifier.
      Available strategies = {"LightGBM", "RandomForest", "ExtraTrees",
      "Tree", "Bagging", "AdaBoost" or "Linear"}.
  
  **params : default = None
      Parameters of the corresponding classifier.
      Examples : n_estimators, max_depth...
class_docstr_tok: ["Wraps","scikitlearn","classifiers",".","Parameters","--","--","--","--","--","strategy",":","str",",","default","=","``","LightGBM","''","The","choice","for","the","classifier",".","Available","strategies","=","{","``","LightGBM","''",",","``","RandomForest","''",",","``","ExtraTrees","''",",","``","Tree","''",",","``","Bagging","''",",","``","AdaBoost","''","or","``","Linear","''","}",".","*","*","params",":","default","=","None","Parameters","of","the","corresponding","classifier",".","Examples",":","n_estimators",",","max_depth","..."]
docstr: Get strategy parameters of Classifier object.
docstr_tok: ["Get","strategy","parameters","of","Classifier","object","."]
code: def get_params(self, deep=True):
      """Get strategy parameters of Classifier object."""
      params = {}
      params["strategy"] = self.__strategy
      params.update(self.__classif_params)
  
      return params
  
file_path: MLBox/mlbox/model/classification/classifier.py
imports: import warnings
  from copy import copy
  import numpy
  import pandas
  from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.tree import DecisionTreeClassifier
  from lightgbm import LGBMClassifier
label_desc: Use image node_id 2 for calling the Classifier obj's underlying member method code with example usage: obj.get_params(deep) and returns: params