code: def false_possitives_negatives(self):
      f1 = f1_score(self.y_test, self.y_pred, average="micro")
      accuracy = accuracy_score(self.y_test, self.y_pred)
      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()
      FN = FN.astype(float).sum()
      TP = TP.astype(float).sum()
      TN = TN.astype(float).sum()
  
      # Sensitivity, hit rate, recall, or true positive rate
      TPR = TP / (TP + FN)
      # Specificity or true negative rate
      TNR = TN / (TN + FP)
      # Precision or positive predictive value
      PPV = TP / (TP + FP)
      # Negative predictive value
      NPV = TN / (TN + FN)
      # Fall out or false positive rate
      FPR = FP / (FP + TN)
      # False negative rate
      FNR = FN / (TP + FN)
      # False discovery rate
      FDR = FP / (TP + FP)
      # Overall accuracy for each class
      ACC = (TP + TN) / (TP + FP + FN + TN)
  
      fp_fn_table = pd.DataFrame(
          dict(
              {
                  "False Possitives (%)": [(FPR * 100).round(2)],
                  "False Negatives (%)": [(FNR * 100).round(2)],
                  "Accuracy (%)": accuracy.round(2) * 100,
                  "F1": f1.round(2),
              }
          )
      )
  
      fig_metrics_table = ff.create_table(
          fp_fn_table, 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 2 for calling the performance_metrics obj's underlying member method code with example usage: obj.false_possitives_negatives() and returns: fig_metrics_table