21.1 Classifier performance & fairness (1): False positives & negatives

• Above: Correct Classification Rate (CCR) [as opposed to Error Rate]
  • For what proportion of the units does the correct output \(y_{i}\) match the classifier (predicted) output \(\hat{y}_{i}\)?
  • Proportion of reoffenders correctly predicted
• False Positive Rate (FPR)
  • For what proportion of the units for which the correct output \(y_{i}\) is negative is the classifier output \(\hat{y}_{i}\) positive?
  • Proportion of NON reoffenders (NON recidivators) predicted as reoffenders (recidivators)
• What then is the False Negative Rate (FNR) be?