{"action":"created","issue":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/6568","repository_url":"https://api.github.com/repos/scikit-learn/scikit-learn","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/6568/labels{/name}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/6568/comments","events_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/6568/events","html_url":"https://github.com/scikit-learn/scikit-learn/issues/6568","id":142243229,"number":6568,"title":"Balanced/Weighted Sampling ","user":{"login":"anjishnu","id":2019824,"avatar_url":"https://avatars.githubusercontent.com/u/2019824?v=3","gravatar_id":"","url":"https://api.github.com/users/anjishnu","html_url":"https://github.com/anjishnu","followers_url":"https://api.github.com/users/anjishnu/followers","following_url":"https://api.github.com/users/anjishnu/following{/other_user}","gists_url":"https://api.github.com/users/anjishnu/gists{/gist_id}","starred_url":"https://api.github.com/users/anjishnu/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/anjishnu/subscriptions","organizations_url":"https://api.github.com/users/anjishnu/orgs","repos_url":"https://api.github.com/users/anjishnu/repos","events_url":"https://api.github.com/users/anjishnu/events{/privacy}","received_events_url":"https://api.github.com/users/anjishnu/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":4,"created_at":"2016-03-21T03:14:42Z","updated_at":"2016-03-21T16:27:16Z","closed_at":null,"body":"Many classification applications need to deal with skewed input data - recently for several projects I've had to implement techniques to re-weight samples during training to get the best results - this can ideally be supported generically by scikit-learn in https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_validation.py\r\n\r\nIn my use case I was able to get significantly better results by assuming a uniform prior during training with the skewed labels- but it makes sense to have a generic way to add weights to the sampled training distribution for cases where researchers have good reason to incorporate a certain prior. "},"comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/comments/199366380","html_url":"https://github.com/scikit-learn/scikit-learn/issues/6568#issuecomment-199366380","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/6568","id":199366380,"user":{"login":"anjishnu","id":2019824,"avatar_url":"https://avatars.githubusercontent.com/u/2019824?v=3","gravatar_id":"","url":"https://api.github.com/users/anjishnu","html_url":"https://github.com/anjishnu","followers_url":"https://api.github.com/users/anjishnu/followers","following_url":"https://api.github.com/users/anjishnu/following{/other_user}","gists_url":"https://api.github.com/users/anjishnu/gists{/gist_id}","starred_url":"https://api.github.com/users/anjishnu/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/anjishnu/subscriptions","organizations_url":"https://api.github.com/users/anjishnu/orgs","repos_url":"https://api.github.com/users/anjishnu/repos","events_url":"https://api.github.com/users/anjishnu/events{/privacy}","received_events_url":"https://api.github.com/users/anjishnu/received_events","type":"User","site_admin":false},"created_at":"2016-03-21T16:27:16Z","updated_at":"2016-03-21T16:27:16Z","body":"Yes, that PR should cover my use case. "}}