{"action":"opened","issue":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7391","repository_url":"https://api.github.com/repos/scikit-learn/scikit-learn","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7391/labels{/name}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7391/comments","events_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7391/events","html_url":"https://github.com/scikit-learn/scikit-learn/issues/7391","id":176298743,"number":7391,"title":"True positive, false positive, false negative, true negative count , TPR, FPR, accuracy, precision, recal, auc, mse, mael","user":{"login":"vinayakumarr","id":18217467,"avatar_url":"https://avatars.githubusercontent.com/u/18217467?v=3","gravatar_id":"","url":"https://api.github.com/users/vinayakumarr","html_url":"https://github.com/vinayakumarr","followers_url":"https://api.github.com/users/vinayakumarr/followers","following_url":"https://api.github.com/users/vinayakumarr/following{/other_user}","gists_url":"https://api.github.com/users/vinayakumarr/gists{/gist_id}","starred_url":"https://api.github.com/users/vinayakumarr/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/vinayakumarr/subscriptions","organizations_url":"https://api.github.com/users/vinayakumarr/orgs","repos_url":"https://api.github.com/users/vinayakumarr/repos","events_url":"https://api.github.com/users/vinayakumarr/events{/privacy}","received_events_url":"https://api.github.com/users/vinayakumarr/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-09-12T05:44:48Z","updated_at":"2016-09-12T05:44:48Z","closed_at":null,"body":"scikit support for calculating accuracy, precision, recall, mse and mae for multi-class classification. but i want the count of true positive, true negative, false positive, false negative, true positive rate, false posititve rate and auc. How to calculate this? is there any in-built functions in scikit. while searching in google i got confused. This is not the place to post this question. But as a begginner i didn't find any other way. Please can you help for calculating these performance measures for multi-class classification"}}
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