The decision rule being "the result is the one with the highest probability", it is possible to avoid computation so that this previous decision rule is equivalent to "the result is the one with highest score", as long as the mathematical transformation from probability to score to not change the top class.
Bertrand Dechoux On Thu, May 12, 2016 at 4:40 AM, Andrew Palumbo <ap....@outlook.com> wrote: > Hello, the elements of the vector are not actually probabilities, they are > scores, the classification is a winner takes all approach, assigning the > classification to the class with the max score. > > See: http://mahout.apache.org/users/algorithms/spark-naive-bayes.html for > an overview of the algorithm. > > Thanks > > ________________________________________ > From: Nantia Makrynioti <nantiam...@gmail.com> > Sent: Wednesday, May 11, 2016 10:33:29 PM > To: user@mahout.apache.org > Subject: Negative probabilities > > Hello, > > I am using the classifyFullInstance method on a Naive Bayes model, but when > I print the elements of the generated vector, the probabilities are > negative. What might be the reason for this? > > Thanks a lot, > Nantia >
