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Naive Bayes is Not So Naiventitled

  • Very Fast Learning and Testing (basically just count the data)

  • Low Storage requirements

  • Very good in domains with many equally important features

  • More robust to irrelevant features than many learning methods

    • Irrelevant Features cancel each other without affecting results

  • More robust to concept drift (changing class definition over time)

  • Naive Bayes won 1st and 2nd place in KDD-CUP 97 competition out of 16 systems

    • Goal: Financial services industry direct mail response prediction: Predict if the recipient of mail will actually respond to the advertisement – 750,000 records.

  • A good dependable baseline for text classification (but not the best)!


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