{"action":"created","issue":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/5037","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/5037/labels{/name}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/5037/comments","events_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/5037/events","html_url":"https://github.com/scikit-learn/scikit-learn/pull/5037","id":97483017,"number":5037,"title":"[MRG+1] Trees as feature transformers","user":{"login":"betatim","id":1448859,"avatar_url":"https://avatars.githubusercontent.com/u/1448859?v=3","gravatar_id":"","url":"https://api.github.com/users/betatim","html_url":"https://github.com/betatim","followers_url":"https://api.github.com/users/betatim/followers","following_url":"https://api.github.com/users/betatim/following{/other_user}","gists_url":"https://api.github.com/users/betatim/gists{/gist_id}","starred_url":"https://api.github.com/users/betatim/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/betatim/subscriptions","organizations_url":"https://api.github.com/users/betatim/orgs","repos_url":"https://api.github.com/users/betatim/repos","events_url":"https://api.github.com/users/betatim/events{/privacy}","received_events_url":"https://api.github.com/users/betatim/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":23,"created_at":"2015-07-27T14:55:16Z","updated_at":"2015-08-31T21:12:03Z","closed_at":"2015-08-31T15:43:49Z","pull_request":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/5037","html_url":"https://github.com/scikit-learn/scikit-learn/pull/5037","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/5037.diff","patch_url":"https://github.com/scikit-learn/scikit-learn/pull/5037.patch"},"body":"This is an example using ensembles of trees to show how to use them to transform your samples into a high dimensional, sparse feature space and then train a linear model on that. In particular how to use the `apply` method of a `DecisionTree`. It came about from a discussion in #4488 and #4549. It is loosely based on @ogrisel's notebook here: http://nbviewer.ipython.org/github/ogrisel/notebooks/blob/master/sklearn_demos/Income%20classification.ipynb\r\n\r\nIn #4549 there is talk of showing the difference between a linear model and PCA. Not quite sure I got what you meant.\r\n\r\nThis could interest @amueller, @ogrisel and @vene."},"comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/comments/136502225","html_url":"https://github.com/scikit-learn/scikit-learn/pull/5037#issuecomment-136502225","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/5037","id":136502225,"user":{"login":"betatim","id":1448859,"avatar_url":"https://avatars.githubusercontent.com/u/1448859?v=3","gravatar_id":"","url":"https://api.github.com/users/betatim","html_url":"https://github.com/betatim","followers_url":"https://api.github.com/users/betatim/followers","following_url":"https://api.github.com/users/betatim/following{/other_user}","gists_url":"https://api.github.com/users/betatim/gists{/gist_id}","starred_url":"https://api.github.com/users/betatim/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/betatim/subscriptions","organizations_url":"https://api.github.com/users/betatim/orgs","repos_url":"https://api.github.com/users/betatim/repos","events_url":"https://api.github.com/users/betatim/events{/privacy}","received_events_url":"https://api.github.com/users/betatim/received_events","type":"User","site_admin":false},"created_at":"2015-08-31T21:12:03Z","updated_at":"2015-08-31T21:12:03Z","body":"Thanks! Now back to walking in the swiss alps: :mount_fuji: "}}