{"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/55278106","id":55278106,"diff_hunk":"@@ -234,15 +234,34 @@ Randomized sparse models\n \n .. currentmodule:: sklearn.linear_model\n \n-The limitation of L1-based sparse models is that faced with a group of\n-very correlated features, they will select only one. To mitigate this\n-problem, it is possible to use randomization techniques, reestimating the\n-sparse model many times perturbing the design matrix or sub-sampling data\n-and counting how many times a given regressor is selected.\n+In terms of feature selection, there are some well-known limitations of\n+L1-penalized models for regression and classification. For example, it is\n+known that the Lasso will tend to select an individual variable out of a group\n+of highly correlated features. Furthermore, even when the correlation between\n+features is not too high, the conditions under which L1-penalized methods\n+consistently select \"good\" features can be restrictive in general.\n+\n+To mitigate this problem, it is possible to use randomization techniques such\n+as those presented in [B2009]_ and [M2010]_. The latter technique, known as\n+stability selection, is implemented in the module :mod:`sklearn.linear_model`.\n+In the stability selection method, a subsample of the data is fit to a\n+L1-penalized model where the penalty of a random subset of coefficients has\n+been scaled. Specifically, given a subsample of the data\n+:math:`(y_i, x_i), i \\in I`, where :math:`I \\subset \\{1, 2, \\ldots, n\\}` is a","path":"doc/modules/feature_selection.rst","position":null,"original_position":33,"commit_id":"ae68dc6c719c236668ef0c943d99cdf1c101baab","original_commit_id":"ebfdc51dee0188d600cec4bf22244fcfeeeb3875","user":{"login":"agramfort","id":161052,"avatar_url":"https://avatars.githubusercontent.com/u/161052?v=3","gravatar_id":"","url":"https://api.github.com/users/agramfort","html_url":"https://github.com/agramfort","followers_url":"https://api.github.com/users/agramfort/followers","following_url":"https://api.github.com/users/agramfort/following{/other_user}","gists_url":"https://api.github.com/users/agramfort/gists{/gist_id}","starred_url":"https://api.github.com/users/agramfort/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/agramfort/subscriptions","organizations_url":"https://api.github.com/users/agramfort/orgs","repos_url":"https://api.github.com/users/agramfort/repos","events_url":"https://api.github.com/users/agramfort/events{/privacy}","received_events_url":"https://api.github.com/users/agramfort/received_events","type":"User","site_admin":false},"body":"nothing to add\n","created_at":"2016-03-07T21:47:14Z","updated_at":"2016-03-07T21:47:14Z","html_url":"https://github.com/scikit-learn/scikit-learn/pull/6498#discussion_r55278106","pull_request_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/6498","_links":{"self":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/55278106"},"html":{"href":"https://github.com/scikit-learn/scikit-learn/pull/6498#discussion_r55278106"},"pull_request":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/6498"}}},"pull_request":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/6498","id":61862090,"html_url":"https://github.com/scikit-learn/scikit-learn/pull/6498","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/6498.diff","patch_url":"https://github.com/scikit-learn/scikit-learn/pull/6498.patch","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/6498","number":6498,"state":"open","locked":false,"title":"[MRG+1] Fix to documentation and docstring of randomized lasso and randomized logistic regression","user":{"login":"clamus","id":5489720,"avatar_url":"https://avatars.githubusercontent.com/u/5489720?v=3","gravatar_id":"","url":"https://api.github.com/users/clamus","html_url":"https://github.com/clamus","followers_url":"https://api.github.com/users/clamus/followers","following_url":"https://api.github.com/users/clamus/following{/other_user}","gists_url":"https://api.github.com/users/clamus/gists{/gist_id}","starred_url":"https://api.github.com/users/clamus/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/clamus/subscriptions","organizations_url":"https://api.github.com/users/clamus/orgs","repos_url":"https://api.github.com/users/clamus/repos","events_url":"https://api.github.com/users/clamus/events{/privacy}","received_events_url":"https://api.github.com/users/clamus/received_events","type":"User","site_admin":false},"body":"Fixes #6493 \r\n\r\n@hlin117 and @agramfort, this is ready.\r\n\r\nTodo:\r\n- [ ] Agree on the notation for `scaling` in relation the equations.\r\n- [x] Agree on \"See Also\" if cross referencing between logistic and lasso sides need to be 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