{"action":"created","issue":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/4161","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/4161/labels{/name}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/4161/comments","events_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/4161/events","html_url":"https://github.com/scikit-learn/scikit-learn/pull/4161","id":55448403,"number":4161,"title":"[MRG] EnsembleClassifier implementation","user":{"login":"rasbt","id":5618407,"avatar_url":"https://avatars.githubusercontent.com/u/5618407?v=3","gravatar_id":"","url":"https://api.github.com/users/rasbt","html_url":"https://github.com/rasbt","followers_url":"https://api.github.com/users/rasbt/followers","following_url":"https://api.github.com/users/rasbt/following{/other_user}","gists_url":"https://api.github.com/users/rasbt/gists{/gist_id}","starred_url":"https://api.github.com/users/rasbt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/rasbt/subscriptions","organizations_url":"https://api.github.com/users/rasbt/orgs","repos_url":"https://api.github.com/users/rasbt/repos","events_url":"https://api.github.com/users/rasbt/events{/privacy}","received_events_url":"https://api.github.com/users/rasbt/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":44,"created_at":"2015-01-26T05:39:41Z","updated_at":"2015-02-25T00:51:48Z","closed_at":null,"pull_request":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/4161","html_url":"https://github.com/scikit-learn/scikit-learn/pull/4161","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/4161.diff","patch_url":"https://github.com/scikit-learn/scikit-learn/pull/4161.patch"},"body":"This is an implementation of an estimator to combine scikit-learn classification estimators into an ensemble classifier for majority rule class label prediction or classification based on weighted probability averages.\r\n\r\nI added a section at the bottom of the ensemble.rst documentation with a more detailed description and examples.\r\n\r\nI'd be happy to make improvements or adjustments if necessary. Please let me know, I am happy to help!"},"comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/comments/75884082","html_url":"https://github.com/scikit-learn/scikit-learn/pull/4161#issuecomment-75884082","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/4161","id":75884082,"user":{"login":"amueller","id":449558,"avatar_url":"https://avatars.githubusercontent.com/u/449558?v=3","gravatar_id":"","url":"https://api.github.com/users/amueller","html_url":"https://github.com/amueller","followers_url":"https://api.github.com/users/amueller/followers","following_url":"https://api.github.com/users/amueller/following{/other_user}","gists_url":"https://api.github.com/users/amueller/gists{/gist_id}","starred_url":"https://api.github.com/users/amueller/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/amueller/subscriptions","organizations_url":"https://api.github.com/users/amueller/orgs","repos_url":"https://api.github.com/users/amueller/repos","events_url":"https://api.github.com/users/amueller/events{/privacy}","received_events_url":"https://api.github.com/users/amueller/received_events","type":"User","site_admin":false},"created_at":"2015-02-25T00:51:48Z","updated_at":"2015-02-25T00:51:48Z","body":"On 02/19/2015 10:16 PM, Sebastian Raschka wrote:\n>\n> Should be all set now except for some long doctests lines, E.g.,\n>\n> |>>> for clf, label in zip([clf1, clf2, clf3, eclf], ['Logistic Regression', 'Random Forest', 'naive Bayes', 'Ensemble']):\n> ...     scores = cross_validation.cross_val_score(clf, X, y, cv=5, scoring='accuracy')\n> ...     print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\" % (scores.mean(), scores.std(), label))\n> |\n>\n> For some reason, I can't wrap the initial line to e.g.,\n>\n> |>>> for clf, label in zip([clf1, clf2, clf3, eclf],\n> ...                                  ['Logistic Regression', 'Random Forest', 'naive Bayes', 'Ensemble']):\n> ...     scores = cross_validation.cross_val_score(clf, X, y, cv=5, scoring='accuracy')\n> ...     print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\" % (scores.mean(), scores.std(), label))\n> |\n>\n> without causing test failures. Any tips for how to go about it?\n>\n>\n#doctest: +NORMALIZE_WHITESPACE\n\n"}}