code: def main(loader, name):
      
  
      scores = []
      raw_scores_ds = {}
  
      # first get the dataset
      X, y, mapping = loader()
  
      clf = linear_model.LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=200, random_state=0)
  
      # try each encoding method available, which works on multiclass problems
      encoders = (set(category_encoders.__all__) - {'WOEEncoder'})  # WoE is currently only for binary targets
  
      for encoder_name in encoders:
          encoder = getattr(category_encoders, encoder_name)
          start_time = time.time()
          score, stds, raw_scores, dim = score_models(clf, X, y, encoder)
          scores.append([encoder_name, name, dim, score, stds, time.time() - start_time])
          raw_scores_ds[encoder_name] = raw_scores
          gc.collect()
  
      results = pd.DataFrame(scores, columns=['Encoding', 'Dataset', 'Dimensionality', 'Avg. Score', 'Score StDev', 'Elapsed Time'])
  
      raw = pd.DataFrame.from_dict(raw_scores_ds)
      ax = raw.plot(kind='box', return_type='axes')
      plt.title('Scores for Encodings on %s Dataset' % (name,))
      plt.ylabel('Score (higher is better)')
      for tick in ax.get_xticklabels():
          tick.set_rotation(90)
      plt.grid()
      plt.tight_layout()
      plt.show()
  
      return results, raw
docs: Here we iterate through the datasets and score them with a classifier using different encodings.