code: def confusionmatrix(truelabels, predictions):
      confusion_matrix = skm.confusion_matrix(truelabels, predictions)
      tn, fp, fn, tp = confusion_matrix.ravel()
      print(
          "Sensitivity: ",
          tp / (tp + fn),
          "\nSpecificity: ",
          tn / (tn + fp),
          "\nPPV: ",
          tp / (tp + fp),
          "\nNPV: ",
          tn / (tn + fn),
      )
  
      skm.ConfusionMatrixDisplay(confusion_matrix).plot()
  
file_path: fairlearn/examples/plot_intersectional_bias.py
imports: import numpy
  import pandas
  import matplotlib.pyplot
  import sklearn.metrics
  import seaborn
  from sklearn.datasets import fetch_openml
  from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
  from sklearn.linear_model import LogisticRegression
  from fairlearn.metrics import MetricFrame, false_positive_rate
label_desc: Use image node_id 3 for calling a global function with example usage: confusionmatrix(truelabels, predictions) without return types