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Model Selection: ROC Curves

  • ROC (Receiver Operating Characteristics) curves: for visual comparison of classification models
  • Originated from signal detection theory
  • Shows the trade-off between the true positive rate and the false positive rate
  • The area under the ROC curve is a measure of the accuracy of the model
  • Rank the test tuples in decreasing order: the one that is most likely to belong to the positive class appears at the top of the list
  • The closer to the diagonal line (i.e., the closer the area is to 0.5), the less accurate is the model
  • Vertical axis represents the true positive rate
  • Horizontal axis rep. the false positive rate

  • The plot also shows a diagonal line
  • A model with perfect accuracy will have an area of 1.0

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