class_docstr: Encodes missing values for both numerical and categorical features.
  
  Several strategies are possible in each case.
  
  Parameters
  ----------
  numerical_strategy : str or float or int. default = "mean"
      The strategy to encode NA for numerical features.
      Available strategies = "mean", "median",
      "most_frequent" or a float/int value
  
  categorical_strategy : str, default = '<NULL>'
      The strategy to encode NA for categorical features.
      Available strategies = a string or "most_frequent"
docstr: Set parameters for a NA_encoder object.
  
  Set numerical strategy and categorical strategy.
  
  Parameters
  ----------
  numerical_strategy : str or float or int. default = "mean"
      The strategy to encode NA for numerical features.
  
  categorical_strategy : str, default = '<NULL>'
      The strategy to encode NA for categorical features.
code: def set_params(self, **params):
      """Set parameters for a NA_encoder object.
  
      Set numerical strategy and categorical strategy.
  
      Parameters
      ----------
      numerical_strategy : str or float or int. default = "mean"
          The strategy to encode NA for numerical features.
  
      categorical_strategy : str, default = '<NULL>'
          The strategy to encode NA for categorical features.
  
      """
      self.__fitOK = False
  
      for k, v in params.items():
          if k not in self.get_params():
              warnings.warn(
                  "Invalid parameter(s) for encoder NA_encoder. "
                  "Parameter(s) IGNORED. "
                  "Check the list of available parameters with "
                  "`encoder.get_params().keys()`"
              )
          else:
              setattr(self, k, v)
  
file_path: MLBox/mlbox/encoding/na_encoder.py
imports: import pandas
  import warnings
  from sklearn.impute import SimpleImputer
label_desc: Use image node_id 3 for calling the NA_encoder obj's underlying member method code with example usage: obj.set_params() without return types