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Class implementing a HyperPipe classifier.



Class implementing a HyperPipe classifier. For each category a HyperPipe is constructed that contains all points of that category (essentially records the attribute bounds observed for each category). Test instances are classified according to the category that "most contains the instance". Does not handle numeric class, or missing values in test cases. Extremely simple algorithm, but has the advantage of being extremely fast, and works quite well when you have "smegloads" of attributes.


  • training set: expects: ExampleSet


  • model:
  • exampleSet:


  • D:
    If set, classifier is run in debug mode and may output additional info to the console
    Range: boolean; default: false

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