{"action":"created","issue":{"url":"https://api.github.com/repos/dmlc/xgboost/issues/3860","repository_url":"https://api.github.com/repos/dmlc/xgboost","labels_url":"https://api.github.com/repos/dmlc/xgboost/issues/3860/labels{/name}","comments_url":"https://api.github.com/repos/dmlc/xgboost/issues/3860/comments","events_url":"https://api.github.com/repos/dmlc/xgboost/issues/3860/events","html_url":"https://github.com/dmlc/xgboost/issues/3860","id":377053437,"node_id":"MDU6SXNzdWUzNzcwNTM0Mzc=","number":3860,"title":"Multilabel implementation","user":{"login":"Jopepato","id":7397474,"node_id":"MDQ6VXNlcjczOTc0NzQ=","avatar_url":"https://avatars2.githubusercontent.com/u/7397474?v=4","gravatar_id":"","url":"https://api.github.com/users/Jopepato","html_url":"https://github.com/Jopepato","followers_url":"https://api.github.com/users/Jopepato/followers","following_url":"https://api.github.com/users/Jopepato/following{/other_user}","gists_url":"https://api.github.com/users/Jopepato/gists{/gist_id}","starred_url":"https://api.github.com/users/Jopepato/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Jopepato/subscriptions","organizations_url":"https://api.github.com/users/Jopepato/orgs","repos_url":"https://api.github.com/users/Jopepato/repos","events_url":"https://api.github.com/users/Jopepato/events{/privacy}","received_events_url":"https://api.github.com/users/Jopepato/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":4,"created_at":"2018-11-03T13:13:59Z","updated_at":"2018-11-08T11:38:11Z","closed_at":null,"author_association":"NONE","body":"Hi, thanks for this marvellous implementation of the algorithm.\r\nI was considering using it for multilabel purposes but as I can see there is no implementation for it.\r\nConsidering the algorithm as treating the labels separately as a BinaryRelevance or even OVO. Should it be implemented inside the library or use it outside, using as a base classifier this xgboost\r\n\r\nIf you are considering implementing it inside, it would be an honour to make the PR! ^"},"comment":{"url":"https://api.github.com/repos/dmlc/xgboost/issues/comments/436965731","html_url":"https://github.com/dmlc/xgboost/issues/3860#issuecomment-436965731","issue_url":"https://api.github.com/repos/dmlc/xgboost/issues/3860","id":436965731,"node_id":"MDEyOklzc3VlQ29tbWVudDQzNjk2NTczMQ==","user":{"login":"Jopepato","id":7397474,"node_id":"MDQ6VXNlcjczOTc0NzQ=","avatar_url":"https://avatars2.githubusercontent.com/u/7397474?v=4","gravatar_id":"","url":"https://api.github.com/users/Jopepato","html_url":"https://github.com/Jopepato","followers_url":"https://api.github.com/users/Jopepato/followers","following_url":"https://api.github.com/users/Jopepato/following{/other_user}","gists_url":"https://api.github.com/users/Jopepato/gists{/gist_id}","starred_url":"https://api.github.com/users/Jopepato/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Jopepato/subscriptions","organizations_url":"https://api.github.com/users/Jopepato/orgs","repos_url":"https://api.github.com/users/Jopepato/repos","events_url":"https://api.github.com/users/Jopepato/events{/privacy}","received_events_url":"https://api.github.com/users/Jopepato/received_events","type":"User","site_admin":false},"created_at":"2018-11-08T11:38:11Z","updated_at":"2018-11-08T11:38:11Z","author_association":"NONE","body":"I was thinking of that as well. Training an X numbers of classifiers one for each label |Y|. But as you say the complexity and time for the model to train all the data would increase a lot...\r\nAnother approach would be as the LabelPowerset does... this is changing the multilabel output to a multiclass problem. Assigning each set of different labels to a class.\r\nBy doing this we lose some information as some set of labels would be unique (mostly in datasets with a high number of labels) but the computational time for the algorithm would be the same as for a multiclass problem."}}
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