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Classifier Evaluation Metrics: Accuracy, Error Rate, Sensitivity and Specificity

  • Classifier Accuracy, or recognition rate: percentage of test set tuples that are correctly classified
    Accuracy = (TP + TN)/All
  • Error rate: 1 – accuracy, or
    Error rate = (FP + FN)/All
  • Class Imbalance Problem:
    • One class may be rare, e.g. fraud, or HIV-positive
    • Significant majority of the negative class and minority of the positive class
    • Sensitivity: True Positive recognition rate
      • Sensitivity = TP/P
    • Specificity: True Negative recognition rate
      • Specificity = TN/N

Speaker notes:

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