{"action":"created","issue":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7757","repository_url":"https://api.github.com/repos/scikit-learn/scikit-learn","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7757/labels{/name}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7757/comments","events_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7757/events","html_url":"https://github.com/scikit-learn/scikit-learn/issues/7757","id":185368949,"number":7757,"title":"StandardScaler that supports option to use unbiased estimator of variance","user":{"login":"spilkjir","id":19406805,"avatar_url":"https://avatars.githubusercontent.com/u/19406805?v=3","gravatar_id":"","url":"https://api.github.com/users/spilkjir","html_url":"https://github.com/spilkjir","followers_url":"https://api.github.com/users/spilkjir/followers","following_url":"https://api.github.com/users/spilkjir/following{/other_user}","gists_url":"https://api.github.com/users/spilkjir/gists{/gist_id}","starred_url":"https://api.github.com/users/spilkjir/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/spilkjir/subscriptions","organizations_url":"https://api.github.com/users/spilkjir/orgs","repos_url":"https://api.github.com/users/spilkjir/repos","events_url":"https://api.github.com/users/spilkjir/events{/privacy}","received_events_url":"https://api.github.com/users/spilkjir/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2016-10-26T11:37:44Z","updated_at":"2016-10-26T19:58:06Z","closed_at":null,"body":"#### Description\r\nIt would be nice to provide StandardScaler with an option to chose unbiased estimator of variance,\r\n\r\nIt it sometimes convenient to use unbiased estimator of variance: numpy.var(X, ddof=1)\r\ninstead of default: numpy.var(X, ddof=0)\r\n\r\nSo far StandardScaler does not allow to set ddof.\r\n\r\n#### Versions\r\nLinux-4.4.0-45-generic-x86_64-with-Ubuntu-16.04-xenial\r\n('Python', '2.7.12 (default, Jul  1 2016, 15:12:24) \\n[GCC 5.4.0 20160609]')\r\n('NumPy', '1.11.2')\r\n('SciPy', '0.18.1')\r\n('Scikit-Learn', '0.19.dev0')\r\n\r\n<!-- Thanks for contributing! -->\r\n"},"comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/comments/256459555","html_url":"https://github.com/scikit-learn/scikit-learn/issues/7757#issuecomment-256459555","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7757","id":256459555,"user":{"login":"spilkjir","id":19406805,"avatar_url":"https://avatars.githubusercontent.com/u/19406805?v=3","gravatar_id":"","url":"https://api.github.com/users/spilkjir","html_url":"https://github.com/spilkjir","followers_url":"https://api.github.com/users/spilkjir/followers","following_url":"https://api.github.com/users/spilkjir/following{/other_user}","gists_url":"https://api.github.com/users/spilkjir/gists{/gist_id}","starred_url":"https://api.github.com/users/spilkjir/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/spilkjir/subscriptions","organizations_url":"https://api.github.com/users/spilkjir/orgs","repos_url":"https://api.github.com/users/spilkjir/repos","events_url":"https://api.github.com/users/spilkjir/events{/privacy}","received_events_url":"https://api.github.com/users/spilkjir/received_events","type":"User","site_admin":false},"created_at":"2016-10-26T19:58:06Z","updated_at":"2016-10-26T19:58:06Z","body":"No problem.\r\nJust to tell the whole story. I've moved my 'pipeline' code from matlab to scikit and spent couple of hours trying to figure out why the matlab and scikit produce different results (selected features and classification performance).\r\n\r\nIt was due to the normalization in StandardScaler.\r\nProviding this option I was just trying to save someone else those couple of hours.\r\n\r\n```\r\nIt seems to me that it's one of those things that people (academics in particular) love to argue, but has no impact on something like predictive accuracy in realistic settings.\r\n```\r\nYes and no :). \r\nI'm sorry, I'm new here, do not want to start an academic discussions. This was only suggestion. \r\n\r\nYou can leave it as it is. I have created my own dirty StandardScaler :),\r\n\r\n\r\n\r\n\r\n"}}
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