{"action":"created","issue":{"url":"https://api.github.com/repos/szilard/benchm-ml/issues/1","labels_url":"https://api.github.com/repos/szilard/benchm-ml/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/szilard/benchm-ml/issues/1/comments","events_url":"https://api.github.com/repos/szilard/benchm-ml/issues/1/events","html_url":"https://github.com/szilard/benchm-ml/issues/1","id":71410664,"number":1,"title":"comment:re sklearn","user":{"login":"TELSER1","id":5139614,"avatar_url":"https://avatars.githubusercontent.com/u/5139614?v=3","gravatar_id":"","url":"https://api.github.com/users/TELSER1","html_url":"https://github.com/TELSER1","followers_url":"https://api.github.com/users/TELSER1/followers","following_url":"https://api.github.com/users/TELSER1/following{/other_user}","gists_url":"https://api.github.com/users/TELSER1/gists{/gist_id}","starred_url":"https://api.github.com/users/TELSER1/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/TELSER1/subscriptions","organizations_url":"https://api.github.com/users/TELSER1/orgs","repos_url":"https://api.github.com/users/TELSER1/repos","events_url":"https://api.github.com/users/TELSER1/events{/privacy}","received_events_url":"https://api.github.com/users/TELSER1/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":3,"created_at":"2015-04-27T21:39:41Z","updated_at":"2015-05-06T21:09:02Z","closed_at":null,"body":"(Your post popped up in my twitter feed)\r\nI'm not sure why you said you needed to one-hot encode categorical variables for scikit's random forest; I'm fairly certain you do not need to(and probably shouldn't).  It's been awhile since I looked at the source, but I'm pretty sure it handles categorical variables encoded as a single vector of numbers just fine from empirical tests; performance is almost always worse if the features were one-hot encoded."},"comment":{"url":"https://api.github.com/repos/szilard/benchm-ml/issues/comments/99610361","html_url":"https://github.com/szilard/benchm-ml/issues/1#issuecomment-99610361","issue_url":"https://api.github.com/repos/szilard/benchm-ml/issues/1","id":99610361,"user":{"login":"szilard","id":844827,"avatar_url":"https://avatars.githubusercontent.com/u/844827?v=3","gravatar_id":"","url":"https://api.github.com/users/szilard","html_url":"https://github.com/szilard","followers_url":"https://api.github.com/users/szilard/followers","following_url":"https://api.github.com/users/szilard/following{/other_user}","gists_url":"https://api.github.com/users/szilard/gists{/gist_id}","starred_url":"https://api.github.com/users/szilard/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/szilard/subscriptions","organizations_url":"https://api.github.com/users/szilard/orgs","repos_url":"https://api.github.com/users/szilard/repos","events_url":"https://api.github.com/users/szilard/events{/privacy}","received_events_url":"https://api.github.com/users/szilard/received_events","type":"User","site_admin":false},"created_at":"2015-05-06T21:09:02Z","updated_at":"2015-05-06T21:09:02Z","body":"I tried it out: 1. Generate integer-encoded categoricals https://gist.github.com/szilard/b2e97062025ac9347f84 2. https://gist.github.com/szilard/56706595b4594e297414\r\n\r\nYou get a >10x speedup, lower memory footprint and increase in AUC (n=1M):\r\n1-hot:    Time: 900s RAM: 20G AUC: 73.2\r\nint-enc:  Time:  60s RAM: 15G AUC: 75.0\r\n\r\nI would expect faster, lower memory but decrease in AUC (or same in same cases). I think the increase in AUC is because 3 variables are actually ordinal (month, day of month and day of week). I should probably use 1-hot encoding to have a fair comparison with the previous results. \r\n\r\n\r\n"}}