class_docstr: Fast LOFO Importance
  Applies already trained model on validation set by noising one feature each time.
  
  Parameters
  ----------
  trained_model: model (sklearn API)
      The model should be trained already
  validation_df: pandas dataframe
  features: list of strings
      List of column names for features within validation_df
  target: string
      Column name for target within validation_df
  scoring: string or callable
      Same as scoring in sklearn API
  n_jobs: int, optional
      Number of jobs for parallel computation
class_docstr_tok: ["Fast","LOFO","Importance","Applies","already","trained","model","on","validation","set","by","noising","one","feature","each","time",".","Parameters","--","--","--","--","--","trained_model",":","model","(","sklearn","API",")","The","model","should","be","trained","already","validation_df",":","pandas","dataframe","features",":","list","of","strings","List","of","column","names","for","features","within","validation_df","target",":","string","Column","name","for","target","within","validation_df","scoring",":","string","or","callable","Same","as","scoring","in","sklearn","API","n_jobs",":","int",",","optional","Number","of","jobs","for","parallel","computation"]
code: def _get_score(self, updated_df):
      return self.scorer(
          self.trained_model,
          updated_df[self.features],
          self.df[self.target],
      )
  
file_path: lofo-importance/lofo/flofo_importance.py
imports: import numpy
  import pandas
  from tqdm.autonotebook import tqdm
  import warnings
  from sklearn.metrics import check_scoring
  from lofo.utils import lofo_to_df, parallel_apply
label_desc: Use image node_id 3 for calling the FLOFOImportance obj's underlying member method code with example usage: obj._get_score(updated_df) and returns: self