2012/7/20 Philipp Singer <[email protected]>: > I jsut have tried out your implementation of semi-supervised > MultinomialNB. The code works flawless, but unfortunately the > performance of the algorithm drops extremely when I trie to incorporate > my additional data. > > I am starting to think that my additional data is useless :/ > > Just for the record: > > training on my 96000 labeled data with MultinomialNB gets me a f1-score > of 0.47. Using around 2.000.000 unlabeled additional data using your > semi-supervised code achieves a f1-score of 0.39
Hmm, too bad. Is the extra data from a very different source? -- Lars Buitinck Scientific programmer, ILPS University of Amsterdam ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
