{"action":"created","issue":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7559","repository_url":"https://api.github.com/repos/scikit-learn/scikit-learn","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7559/labels{/name}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7559/comments","events_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7559/events","html_url":"https://github.com/scikit-learn/scikit-learn/issues/7559","id":180650843,"number":7559,"title":"Tolerance on RFECV","user":{"login":"stevenpawley","id":13805614,"avatar_url":"https://avatars.githubusercontent.com/u/13805614?v=3","gravatar_id":"","url":"https://api.github.com/users/stevenpawley","html_url":"https://github.com/stevenpawley","followers_url":"https://api.github.com/users/stevenpawley/followers","following_url":"https://api.github.com/users/stevenpawley/following{/other_user}","gists_url":"https://api.github.com/users/stevenpawley/gists{/gist_id}","starred_url":"https://api.github.com/users/stevenpawley/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/stevenpawley/subscriptions","organizations_url":"https://api.github.com/users/stevenpawley/orgs","repos_url":"https://api.github.com/users/stevenpawley/repos","events_url":"https://api.github.com/users/stevenpawley/events{/privacy}","received_events_url":"https://api.github.com/users/stevenpawley/received_events","type":"User","site_admin":false},"labels":[{"id":12995801,"url":"https://api.github.com/repos/scikit-learn/scikit-learn/labels/Enhancement","name":"Enhancement","color":"d7e102","default":false},{"id":218978315,"url":"https://api.github.com/repos/scikit-learn/scikit-learn/labels/Need%20Contributor","name":"Need Contributor","color":"009800","default":false}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":8,"created_at":"2016-10-03T14:12:51Z","updated_at":"2016-11-28T22:38:07Z","closed_at":null,"body":"Hi,\n\nIs there any way to specify a tolerance when determining the number of optimal features when using RFECV (like n the CARET package)? Currently when using RFECV with many of the tree-based classifiers, the removal of the least important n features often results in a very slight reduction in model accuracy. This means that all of the features end up being selected as 'optimal', even though the reduction in model accuracy is very slight if we used a much smaller set of features. A tolerance setting, like 1% would allow only the features to be selected that would otherwise cause a large drop in model accuracy.\n\nSteve\n"},"comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/comments/263417226","html_url":"https://github.com/scikit-learn/scikit-learn/issues/7559#issuecomment-263417226","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7559","id":263417226,"user":{"login":"amueller","id":449558,"avatar_url":"https://avatars.githubusercontent.com/u/449558?v=3","gravatar_id":"","url":"https://api.github.com/users/amueller","html_url":"https://github.com/amueller","followers_url":"https://api.github.com/users/amueller/followers","following_url":"https://api.github.com/users/amueller/following{/other_user}","gists_url":"https://api.github.com/users/amueller/gists{/gist_id}","starred_url":"https://api.github.com/users/amueller/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/amueller/subscriptions","organizations_url":"https://api.github.com/users/amueller/orgs","repos_url":"https://api.github.com/users/amueller/repos","events_url":"https://api.github.com/users/amueller/events{/privacy}","received_events_url":"https://api.github.com/users/amueller/received_events","type":"User","site_admin":false},"created_at":"2016-11-28T22:38:07Z","updated_at":"2016-11-28T22:38:07Z","body":"ah it's here: https://topepo.github.io/caret/recursive-feature-elimination.html"}}
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