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 __init__(self, strategy="l1", threshold=0.3):
      # 'variance','l1, 'rf_feature_importance'
      self.strategy = strategy
      # a float between 0. and 1. defaut : 0.3 ie we drop 0.3 of features
      self.threshold = threshold
      self.__fitOK = False
      self.__to_discard = []
  
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 1 to create a new Clf_feature_selector object  with example: obj = Clf_feature_selector(strategy, threshold)