Robust Probabilistic Calibration

  title={Robust Probabilistic Calibration},
  author={Stefan R{\"u}ping},
  • S. Rüping
  • Published in ECML 18 September 2006
  • Computer Science
Probabilistic calibration is the task of producing reliable estimates of the conditional class probability P(class | observation) from the outputs of numerical classifiers. A recent comparative study [1] revealed that Isotonic Regression [2] and Platt Calibration [3] are most effective probabilistic calibration technique for a wide range of classifiers. This paper will demonstrate that these methods are sensitive to outliers in the data. An improved calibration method will be introduced that… 

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Classification rules in standardized partition spaces