code: def actuals(self):
      cnf_matrix = confusion_matrix(self.y_test, self.y_pred)
  
      FP = cnf_matrix.sum(axis=0) - np.diag(cnf_matrix)
      FN = cnf_matrix.sum(axis=1) - np.diag(cnf_matrix)
      TP = np.diag(cnf_matrix)
      TN = cnf_matrix.sum() - (FP + FN + TP)
      fp = FP.astype(float).sum().round()
      fn = FN.astype(float).sum().round()
      tp = TP.astype(float).sum().round()
      tn = TN.astype(float).sum().round()
  
      total = tn + fn + fp + tp
  
      predicted_no = [tn, fn]
      predicted_yes = [fp, tp]
  
      data = {
          "Predicted No": predicted_no,
          "Predicted Yes": predicted_yes,
      }
  
      # Creates pandas DataFrame.
      df = pd.DataFrame(data, index=["No", "Yes"])
      df["Total"] = df.sum(axis=1)
      df.loc["Total", :] = df.sum(axis=0)
      df.reset_index(inplace=True)
      df.rename({"index": "Actual"}, axis=1, inplace=True)
  
      actuals_table = ff.create_table(df, height_constant=15)
  
      return actuals_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 10 for calling the performance_metrics obj's underlying member method code with example usage: obj.actuals() and returns: actuals_table