Cybercrime — Confusion Matrix

  • The classifier made a total of 165 predictions (e.g., 165 patients were being tested for the presence of that disease).
  • Out of those 165 cases, the classifier predicted “yes” 110 times, and “no” 55 times.
  • In reality, 105 patients in the sample have the disease, and 60 patients do not.
  • true negatives (TN): We predicted no, and they don’t have the disease.
  • false positives (FP): We predicted yes, but they don’t actually have the disease. (Also known as a “Type I error.”)
  • false negatives (FN): We predicted no, but they actually do have the disease. (Also known as a “Type II error.”)

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