class_docstr: Selects useful features.
  
  Several strategies are possible (filter and wrapper methods).
  Works for classification problems only (multiclass or binary).
  
  Parameters
  ----------
  strategy : str, defaut = "l1"
      The strategy to select features.
      Available strategies = {"variance", "l1", "rf_feature_importance"}
  
  threshold : float, defaut = 0.3
      The percentage of variable to discard according to the strategy.
      Must be between 0. and 1.
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code: def set_params(self, **params):
      self.__fitOK = False
  
      for k, v in params.items():
          if k not in self.get_params():
              warnings.warn(
                  "Invalid parameter a for feature selector"
                  "Clf_feature_selector. Parameter IGNORED. Check"
                  "the list of available parameters with"
                  "`feature_selector.get_params().keys()`"
              )
          else:
              setattr(self, k, v)
  
file_path: MLBox/mlbox/model/classification/feature_selector.py
imports: import numpy
  import pandas
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import RandomForestClassifier
  import warnings
label_desc: Use image node_id 3 for calling the Clf_feature_selector obj's underlying member method code with example usage: obj.set_params() without return types