code: def performance_metrics_regression(self):
      maxerror = max_error(self.y_test, self.y_pred).round(2)
  
      mae = mean_absolute_error(self.y_test, self.y_pred).round(2)
  
      mse = mean_squared_error(self.y_test, self.y_pred).round(2)
  
      r2 = r2_score(self.y_test, self.y_pred).round(2)
  
      def mean_absolute_percentage_error(y_true, y_pred):
          y_true, y_pred = np.array(y_true), np.array(y_pred)
          return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
  
      mape = mean_absolute_percentage_error(
          self.y_test, self.y_pred
      ).round(2)
  
      # Make DataFrames
      metric = ["Max Error", "R squared", "MAE", "MSE", "MAPE"]
      values = [maxerror, r2, mae, mse, mape]
      metrics_dataframe = pd.DataFrame(
          {"metric": metric, "values": values}
      )
      metrics_dataframe = ff.create_table(
          metrics_dataframe, height_constant=15
      )
      return metrics_dataframe
  
imports: import pandas
  import numpy
  import sklearn
  from sklearn import *
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import label_binarize
  from sklearn.metrics import *
  from itertools import cycle
  import colorlover
  import plotly.figure_factory
  import plotly.graph_objects
label_desc: Use image node_id 9 for calling the performance_metrics obj's underlying member method code with example usage: obj.performance_metrics_regression() and returns: metrics_dataframe, unknown