docs: def visualize_model(X, y, estimator, path, **kwargs):
      """
      Test various estimators.
      """ 
      y = LabelEncoder().fit_transform(y)
      model = Pipeline([
           ('one_hot_encoder', OneHotEncoder()), 
           ('estimator', estimator)
      ])
  
      _, ax = plt.subplots()
  
      # Instantiate the classification model and visualizer
      visualizer = ClassificationReport(
          model, classes=['edible', 'poisonous'], 
          cmap="YlGn", size=(600, 360), ax=ax, **kwargs
      )
      visualizer.fit(X, y)  
      visualizer.score(X, y)
      visualizer.poof(outpath=path)