class_docstr: Selects useful features.
  
  Several strategies are possible (filter and wrapper methods).
  Works for regression problems only.
  
  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 the strategy.
      Must be between 0. and 1.
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code: def get_params(self, deep=True):
      return {"strategy": self.strategy, "threshold": self.threshold}
  
file_path: MLBox/mlbox/model/regression/feature_selector.py
imports: import numpy
  import pandas
  from sklearn.linear_model import Lasso
  from sklearn.ensemble import RandomForestRegressor
  import warnings
label_desc: Use image node_id 2 for calling the Reg_feature_selector obj's underlying member method code with example usage: obj.get_params(deep) and returns: dict