How does the ROC Curve illustrate the performance of a binary classification model?

  • Plots accuracy vs. error rate, shows overall performance
  • Plots precision vs. recall, shows trade-off between sensitivity and specificity
  • Plots true positive rate vs. false positive rate, shows trade-off between sensitivity and specificity
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The ROC Curve plots the true positive rate against the false positive rate for different threshold values. This illustrates the trade-off between sensitivity (true positive rate) and specificity (true negative rate), helping to choose the threshold that best balances these two aspects.
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