code: def metrics_table(self):
      # AUC score
      lb = preprocessing.LabelBinarizer()
      lb.fit(self.y_test)
      y_test_roc = lb.transform(self.y_test)
      y_pred_roc = lb.transform(self.y_pred)
      auc = roc_auc_score(
          y_test_roc, y_pred_roc, average="macro"
      ).round(2)
  
      # MAE
      mae = mean_absolute_error(self.y_test, self.y_pred).round(2)
      # MSE
      mse = mean_squared_error(self.y_test, self.y_pred).round(2)
  
      # Accuracy
      accuracy = accuracy_score(self.y_test, self.y_pred).round(2)
  
      # matthews_corrcoef
      matthews_corrcoe = matthews_corrcoef(
          self.y_test, self.y_pred
      ).round(2)
  
      # Make DataFrames
      metric = [
          "Accuracy",
          "Area Under Curve",
          "MAE",
          "MSE",
          "Matthews Corrcoef",
      ]
      values = [accuracy, auc, mae, mse, matthews_corrcoe]
      metrics_tab = pd.DataFrame({"metric": metric, "values": values})
  
      fig_metrics_table = ff.create_table(
          metrics_tab, height_constant=15
      )
  
      return fig_metrics_table
  
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 8 for calling the performance_metrics obj's underlying member method code with example usage: obj.metrics_table() and returns: fig_metrics_table