Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement

  title={Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement},
  author={George Forman and Martin Scholz},
  journal={SIGKDD Explorations},
Cross-validation is a mainstay for measuring performance and progress in machine learning. There are subtle differences in how exactly to compute accuracy, F-measure and Area Under the ROC Curve (AUC) in cross-validation studies. However, these details are not discussed in the literature, and incompatible methods are used by various papers and software packages. This leads to inconsistency across the research literature. Anomalies in performance calculations for particular folds and situations… CONTINUE READING
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