The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics

  title={The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics},
  author={Peter A. Flach},
Many different metrics are used in machine learning and data mining to build and evaluate models. However, there is no general theory of machine learning metrics, that could answer questions such as: When we simultaneously want to optimise two criteria, how can or should they be traded off? Some metrics are inherently independent of class and misclassification cost distributions, while other are not — can this be made more precise? This paper provides a derivation of ROC space from first… CONTINUE READING
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