{"issue":{"milestone":null,"user":{"id":335383,"login":"larsmans","url":"https://api.github.com/users/larsmans","avatar_url":"https://secure.gravatar.com/avatar/56e17cbc8c5ea5b37d67018db6d030b0?d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-140.png","gravatar_id":"56e17cbc8c5ea5b37d67018db6d030b0"},"created_at":"2011-11-07T22:14:35Z","title":"Semisupervised Naive Bayes using Expectation Maximization","state":"open","updated_at":"2011-12-19T10:04:23Z","labels":[],"id":2168203,"html_url":"https://github.com/scikit-learn/scikit-learn/issues/430","closed_at":null,"pull_request":{"patch_url":"https://github.com/scikit-learn/scikit-learn/pull/430.patch","html_url":"https://github.com/scikit-learn/scikit-learn/pull/430","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/430.diff"},"body":"Here's the EM algorithm for semisupervised Naive Bayes. The implementation checks for convergence based on the coefficients following the advice of Bishop 2006, so it could be used more generally for linear classifier self-training, but I only implemented the necessary machinery (fitting on a 1-of-K vector) on the discrete NB estimators and we might want to switch to log-likelihood based convergence checking later.\r\n\r\nNarrative documentation follows if there's interest in this pull request. I've adapted the document classification example script into a new, semisupervised example. We might also merge these scripts.","number":430,"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/430","comments":15,"assignee":null},"comment":{"user":{"id":335383,"login":"larsmans","url":"https://api.github.com/users/larsmans","avatar_url":"https://secure.gravatar.com/avatar/56e17cbc8c5ea5b37d67018db6d030b0?d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-140.png","gravatar_id":"56e17cbc8c5ea5b37d67018db6d030b0"},"created_at":"2011-12-19T10:04:23Z","updated_at":"2011-12-19T10:04:23Z","id":3201327,"body":"@fannix: I've reproduced the bad performance you saw. This seems to be due to commit b5bc737a6564bc227b510fe4e4cf65c138801a6a, i.e. only re-labeling the unlabeled samples.","url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/comments/3201327"},"action":"created"}