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Natural Language Processing with Java and LingPipe Cookbook by Krishna Dayanidhi, Breck Baldwin

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Getting confidence estimates from a classifier

Classifiers tend to be a lot more useful if they give more information about how confident they are of the classification—this is usually a score or a probability. We often threshold classifiers to help fit the performance requirements of an installation. For example, if it was vital that the classifier never makes a mistake, then we could require that the classification be very confident before committing to a decision.

LingPipe classifiers exist on a hierarchy based on the kinds of estimates they provide. The backbone is a series of interfaces—don't freak out; it is actually pretty simple. You don't need to understand it now, but we do need to write it down somewhere for future reference:

  • BaseClassifier<E> ...

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