code: def main(loader, name):
      """
      Here we iterate through the datasets and score them with a classifier using different encodings.
  
      """
  
      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
  
file_path: category_encoders/examples/encoding_examples.py
imports: import gc
  import time
  import warnings
  import matplotlib.pyplot
  import numpy
  import pandas
  from sklearn import linear_model
  from sklearn.exceptions import DataConversionWarning
  from sklearn.model_selection import cross_validate
  from sklearn.preprocessing import StandardScaler
  import category_encoders
  from examples.source_data.loaders import get_cars_data
label_desc: Use image node_id 2 for calling a global function with example usage: main(loader, name) and returns: results,  raw