{"action":"closed","issue":{"url":"https://api.github.com/repos/pmcphilwalsh/date-a-scientist/issues/1","repository_url":"https://api.github.com/repos/pmcphilwalsh/date-a-scientist","labels_url":"https://api.github.com/repos/pmcphilwalsh/date-a-scientist/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/pmcphilwalsh/date-a-scientist/issues/1/comments","events_url":"https://api.github.com/repos/pmcphilwalsh/date-a-scientist/issues/1/events","html_url":"https://github.com/pmcphilwalsh/date-a-scientist/issues/1","id":376804723,"node_id":"MDU6SXNzdWUzNzY4MDQ3MjM=","number":1,"title":"Bad practice to optimize random state","user":{"login":"mackenzieyoung","id":40209203,"node_id":"MDQ6VXNlcjQwMjA5MjAz","avatar_url":"https://avatars0.githubusercontent.com/u/40209203?v=4","gravatar_id":"","url":"https://api.github.com/users/mackenzieyoung","html_url":"https://github.com/mackenzieyoung","followers_url":"https://api.github.com/users/mackenzieyoung/followers","following_url":"https://api.github.com/users/mackenzieyoung/following{/other_user}","gists_url":"https://api.github.com/users/mackenzieyoung/gists{/gist_id}","starred_url":"https://api.github.com/users/mackenzieyoung/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mackenzieyoung/subscriptions","organizations_url":"https://api.github.com/users/mackenzieyoung/orgs","repos_url":"https://api.github.com/users/mackenzieyoung/repos","events_url":"https://api.github.com/users/mackenzieyoung/events{/privacy}","received_events_url":"https://api.github.com/users/mackenzieyoung/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-11-02T13:12:53Z","updated_at":"2018-11-02T14:21:01Z","closed_at":"2018-11-02T14:21:01Z","author_association":"NONE","body":"https://github.com/pmcphilwalsh/date-a-scientist/blob/f263177371e65387a456ef4f4b0b86b59a5d72c0/pwalsh-capstone/regression-lin-reg.py#L478-L500\r\n\r\nGood idea to look for different ways to optimize your results, but random states should be selected at random, because optimizing the random state will bias your performance measures. Using a particular random_state is meant for reproducing the same results every time you run your code, not for achieving a certain accuracy. Also, if you have a good model, different train/test splits should not make too much of a difference in your results."}}
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