{"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/28575332","id":28575332,"diff_hunk":"@@ -1085,8 +1085,29 @@ Regression metrics\n \n The :mod:`sklearn.metrics` module implements several loss, score, and utility\n functions to measure regression performance. Some of those have been enhanced\n-to handle the multioutput case: :func:`mean_absolute_error`,\n-:func:`mean_squared_error`, :func:`median_absolute_error` and :func:`r2_score`.\n+to handle the multioutput case: :func:`mean_squared_error`,\n+:func:`mean_absolute_error`, :func:`explained_variance_score` and\n+:func:`r2_score`.\n+\n+\n+These functions have an ``multioutput`` keyword argument which specifies\n+the way the scores for each individual target should be averaged. The default\n+is ``'uniform_average'``, which entails a uniformly weighted mean over outputs. If\n+an ``ndarray`` of shape ``(n_outputs,)`` is passed, then its entries are\n+interpreted as weights and an according weighted average is returned. If\n+``multioutput`` is ``'raw_scores'`` is specified, then all unaltered individual scores\n+will be returned in an array of shape ``(n_outputs,)``.\n+\n+\n+The :func:`r2_score` and :func:`explained_variance_score` additionally\n+accept ``multioutput='variance_weighted'``, which will lead to a weighting of\n+each individual score by the variance of the corresponding target variable.\n+This setting quantifies the globally captured unscaled variance. If the\n+target variables are of different scale, then this score puts more\n+importance on well explaining the higher variance variables.\n+Now it is default value for :func:`r2_score` due to backward compatibility.","path":"doc/modules/model_evaluation.rst","position":26,"original_position":26,"commit_id":"d0b8ba1346e30f4f3c9d6196ea9f74935aa97b69","original_commit_id":"d0b8ba1346e30f4f3c9d6196ea9f74935aa97b69","user":{"login":"kshmelkov","id":10819534,"avatar_url":"https://avatars.githubusercontent.com/u/10819534?v=3","gravatar_id":"","url":"https://api.github.com/users/kshmelkov","html_url":"https://github.com/kshmelkov","followers_url":"https://api.github.com/users/kshmelkov/followers","following_url":"https://api.github.com/users/kshmelkov/following{/other_user}","gists_url":"https://api.github.com/users/kshmelkov/gists{/gist_id}","starred_url":"https://api.github.com/users/kshmelkov/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kshmelkov/subscriptions","organizations_url":"https://api.github.com/users/kshmelkov/orgs","repos_url":"https://api.github.com/users/kshmelkov/repos","events_url":"https://api.github.com/users/kshmelkov/events{/privacy}","received_events_url":"https://api.github.com/users/kshmelkov/received_events","type":"User","site_admin":false},"body":"What do you mean? Explicit what?","created_at":"2015-04-17T07:43:01Z","updated_at":"2015-04-17T07:43:01Z","html_url":"https://github.com/scikit-learn/scikit-learn/pull/4491#discussion_r28575332","pull_request_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/4491","_links":{"self":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/28575332"},"html":{"href":"https://github.com/scikit-learn/scikit-learn/pull/4491#discussion_r28575332"},"pull_request":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/4491"}}},"pull_request":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/4491","id":32501184,"html_url":"https://github.com/scikit-learn/scikit-learn/pull/4491","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/4491.diff","patch_url":"https://github.com/scikit-learn/scikit-learn/pull/4491.patch","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/4491","number":4491,"state":"open","locked":false,"title":"[MRG] rebasing pr/3474 (multioutput regression metrics)","user":{"login":"kshmelkov","id":10819534,"avatar_url":"https://avatars.githubusercontent.com/u/10819534?v=3","gravatar_id":"","url":"https://api.github.com/users/kshmelkov","html_url":"https://github.com/kshmelkov","followers_url":"https://api.github.com/users/kshmelkov/followers","following_url":"https://api.github.com/users/kshmelkov/following{/other_user}","gists_url":"https://api.github.com/users/kshmelkov/gists{/gist_id}","starred_url":"https://api.github.com/users/kshmelkov/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kshmelkov/subscriptions","organizations_url":"https://api.github.com/users/kshmelkov/orgs","repos_url":"https://api.github.com/users/kshmelkov/repos","events_url":"https://api.github.com/users/kshmelkov/events{/privacy}","received_events_url":"https://api.github.com/users/kshmelkov/received_events","type":"User","site_admin":false},"body":"I have tried to rebase PR #3474. If it passes Travis, I guess mentionned PR can be replaced.\r\n\r\nBasically, it is a support of multioutput support for regression metrics (MAE, MSE, R2, explained variance) in a way that we can get a whole array of scores instead of some kind of averaging. BTW, I corrected a small mistake in docs: it states that median absolute error supports multioutput while it 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