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      • Computer Science Working Paper Series
      • 2003 Working Papers
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      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2003 Working Papers
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      Visualizing class probability estimators

      Frank, Eibe; Hall, Mark A.
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      Frank, E. & Hall, M. (2003). Visualizing class probability estimators. (Working paper 02/03). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1010
      Abstract
      Inducing classifiers that make accurate predictions on future data is a driving force for research in inductive learning. However, also of importance to the users is how to gain information from the models produced. Unfortunately, some of the most powerful inductive learning algorithms generate "black boxes"—that is, the representation of the model makes it virtually impossible to gain any insight into what has been learned. This paper presents a technique that can help the user understand why a classifier makes the predictions that it does by providing a two-dimensional visualization of its class probability estimates. It requires the classifier to generate class probabilities but most practical algorithms are able to do so (or can be modified to this end).
      Date
      2003-02-19
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      02/03
      Publisher
      University of Waikato, Department of Computer Science
      Collections
      • 2003 Working Papers [8]
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