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: Set the classifier using scikitlearn Classifier.
docstr_tok: ["Set","the","classifier","using","scikitlearn","Classifier","."]
code: def __set_classifier(self, strategy):
      """Set the classifier using scikitlearn Classifier."""
      self.__strategy = strategy
  
      if strategy == "RandomForest":
          self.__classifier = RandomForestClassifier(
              n_estimators=400,
              max_depth=10,
              max_features="sqrt",
              bootstrap=True,
              n_jobs=-1,
              random_state=0,
          )
  
      elif strategy == "LightGBM":
          self.__classifier = LGBMClassifier(
              n_estimators=500,
              learning_rate=0.05,
              colsample_bytree=0.8,
              subsample=0.9,
              nthread=-1,
              seed=0,
          )
  
      elif strategy == "ExtraTrees":
          self.__classifier = ExtraTreesClassifier(
              n_estimators=400,
              max_depth=10,
              max_features="sqrt",
              bootstrap=True,
              n_jobs=-1,
              random_state=0,
          )
  
      elif strategy == "Tree":
          self.__classifier = DecisionTreeClassifier(
              criterion="gini",
              splitter="best",
              max_depth=None,
              min_samples_split=2,
              min_samples_leaf=1,
              min_weight_fraction_leaf=0.0,
              max_features=None,
              random_state=0,
              max_leaf_nodes=None,
              class_weight=None,
              presort=False,
          )
  
      elif strategy == "Bagging":
          self.__classifier = BaggingClassifier(
              base_estimator=None,
              n_estimators=500,
              max_samples=0.9,
              max_features=0.85,
              bootstrap=False,
              bootstrap_features=False,
              n_jobs=-1,
              random_state=0,
          )
  
      elif strategy == "AdaBoost":
          self.__classifier = AdaBoostClassifier(
              base_estimator=None,
              n_estimators=400,
              learning_rate=0.05,
              algorithm="SAMME.R",
              random_state=0,
          )
  
      elif strategy == "Linear":
          self.__classifier = LogisticRegression(
              penalty="l2",
              dual=False,
              tol=0.0001,
              C=1.0,
              fit_intercept=True,
              intercept_scaling=1,
              class_weight=None,
              random_state=0,
              solver="lbfgs",
              max_iter=100,
              multi_class="ovr",
              verbose=0,
              warm_start=False,
              n_jobs=-1,
          )
  
      else:
          raise ValueError(
              "Strategy invalid. Please choose between 'LightGBM'"
              ", 'RandomForest', 'ExtraTrees', "
              "'Tree', 'Bagging', 'AdaBoost' or 'Linear'"
          )
  
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 4 for calling the Classifier obj's underlying member method code with example usage: obj.__set_classifier(strategy) without return types