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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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