{"issue":{"title":"MRG: Feature importances with trees","user":{"login":"glouppe","id":477771,"url":"https://api.github.com/users/glouppe","gravatar_id":"ce5c9f91125ac19e79cc826a13e5dc1e","avatar_url":"https://secure.gravatar.com/avatar/ce5c9f91125ac19e79cc826a13e5dc1e?d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-140.png"},"created_at":"2011-12-19T12:13:53Z","milestone":null,"labels":[],"state":"open","updated_at":"2011-12-28T18:48:07Z","id":2599492,"pull_request":{"patch_url":"https://github.com/scikit-learn/scikit-learn/pull/478.patch","html_url":"https://github.com/scikit-learn/scikit-learn/pull/478","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/478.diff"},"closed_at":null,"html_url":"https://github.com/scikit-learn/scikit-learn/issues/478","body":"This PR implements feature importances using single tree and/or forest of trees.\r\n\r\nIt comes with doc, tests and two examples.\r\n\r\nReviews are welcome :)","number":478,"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/478","comments":39,"assignee":null},"comment":{"user":{"login":"agramfort","id":161052,"url":"https://api.github.com/users/agramfort","gravatar_id":"e9758ca665b8c3d8cb3026c114d05833","avatar_url":"https://secure.gravatar.com/avatar/e9758ca665b8c3d8cb3026c114d05833?d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-140.png"},"created_at":"2011-12-28T18:48:07Z","updated_at":"2011-12-28T18:48:07Z","id":3293787,"body":"I am coming a bit too late in the discussion but if I can still add\nsomething, the univariate selection also has a similar transform:\n\nhttps://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_selection/univariate_selection.py#L249\n\nit also adds an inverse transform that is also leaking (duplication\nfor now) in the current PR on randomized linear models. In this design\nit uses the get_support method. This makes me think that feature\nscoring with tree could also be exposed as a simple function like\nf_oneway which could also be cached for efficient use in a Pipeline\nusing the Select* classes. With such a design the logic of feature\nselection (percentile, median etc.) would be shared with the\nunivariate feature selection code.","url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/comments/3293787"},"action":"created"}