{"issue":{"created_at":"2011-11-12T10:38:50Z","state":"open","updated_at":"2011-11-23T15:49:46Z","comments":48,"user":{"gravatar_id":"ce5c9f91125ac19e79cc826a13e5dc1e","id":477771,"url":"https://api.github.com/users/glouppe","login":"glouppe","avatar_url":"https://secure.gravatar.com/avatar/ce5c9f91125ac19e79cc826a13e5dc1e?d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-140.png"},"body":"This is a work-in-progress pull request for forests of randomized trees. \r\n\r\nIt includes the following changes:\r\n- Added BaseEnsemble from which ensemble classes should be derived\r\n- Added RandomForestClassifier/Regressor (ensemble)\r\n- Added ExtraTreeClassifier/Regressor (tree)\r\n- Added ExtraTreesClassifier/Regressor (ensemble)\r\n- Added tests (coverage of the ensemble module is 100%)\r\n- Added an example comparing the surface decision of a decision tree, of a random forest and of an extra-trees classifier\r\n- Added narrative documentation\r\n- Improved some minor details in the tree module","assignee":null,"id":2216641,"number":439,"pull_request":{"patch_url":"https://github.com/scikit-learn/scikit-learn/pull/439.patch","html_url":"https://github.com/scikit-learn/scikit-learn/pull/439","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/439.diff"},"labels":[],"title":"Ensemble: Forests of randomized trees ","closed_at":null,"html_url":"https://github.com/scikit-learn/scikit-learn/issues/439","milestone":null,"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/439"},"action":"created","comment":{"created_at":"2011-11-23T15:49:46Z","updated_at":"2011-11-23T15:49:46Z","user":{"gravatar_id":"e9758ca665b8c3d8cb3026c114d05833","id":161052,"url":"https://api.github.com/users/agramfort","login":"agramfort","avatar_url":"https://secure.gravatar.com/avatar/e9758ca665b8c3d8cb3026c114d05833?d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-140.png"},"body":"+1 for merge.\n\nI've a few pep8 + pyflakes warnings fixed on my copy but I'll fix it in master.\n\nfor the future, it would be great to have an n_jobs parameter so the\ntrees can be fit\nin parallel with joblib. Feel free to create the issue once merged.","id":2850320,"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/comments/2850320"}}