{"action":"created","comment":{"url":"https://api.github.com/repos/qiime2/docs/pulls/comments/209840747","pull_request_review_id":145930640,"id":209840747,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIwOTg0MDc0Nw==","diff_hunk":"@@ -30,9 +30,9 @@ Supervised learning classifiers predict the categorical metadata classes of unla\n \n Next, we will train and test a classifier that predicts which body site a sample originated from based on its microbial composition. We will do so using the ``classify-samples`` pipeline, which performs a series of steps under the hood:\n \n-1. The input samples are randomly split into a ``training`` set and a ``test`` set. The test set is held out until the end of the pipeline, allowing us to test accuracy on a set of samples that was not used for model training. The fraction of test samples to remove is adjusted with the ``--p-test-size`` parameter.\n+1. The input samples are randomly split into a ``training`` set and a ``test`` set. The test set is held out until the end of the pipeline, allowing us to test accuracy on a set of samples that was not used for model training. The fraction of input samples to include in the test set is adjusted with the ``--p-test-size`` parameter.\n \n-2. We train the learning model using the training set samples. The model is trained to predict a specific ``target`` value for each sample (contained in a metadata column) based on the feature data associated with that sample.\n+2. We train the learning model using the training set samples. The model is trained to predict a specific ``target`` value for each sample (contained in a metadata column) based on the feature data associated with that sample. A range of different learning models can be selected using the ``estimator`` parameter; more details on individual estimators can be found in the `scikit-learn documentation`_.","path":"source/tutorials/sample-classifier.rst","position":8,"original_position":8,"commit_id":"3740f82818ad7eff0f9137717a1afbbf82d10399","original_commit_id":"3740f82818ad7eff0f9137717a1afbbf82d10399","user":{"login":"BenKaehler","id":5283007,"node_id":"MDQ6VXNlcjUyODMwMDc=","avatar_url":"https://avatars3.githubusercontent.com/u/5283007?v=4","gravatar_id":"","url":"https://api.github.com/users/BenKaehler","html_url":"https://github.com/BenKaehler","followers_url":"https://api.github.com/users/BenKaehler/followers","following_url":"https://api.github.com/users/BenKaehler/following{/other_user}","gists_url":"https://api.github.com/users/BenKaehler/gists{/gist_id}","starred_url":"https://api.github.com/users/BenKaehler/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/BenKaehler/subscriptions","organizations_url":"https://api.github.com/users/BenKaehler/orgs","repos_url":"https://api.github.com/users/BenKaehler/repos","events_url":"https://api.github.com/users/BenKaehler/events{/privacy}","received_events_url":"https://api.github.com/users/BenKaehler/received_events","type":"User","site_admin":false},"body":"I personally think of \"models\" as being what we develop when we've got some mechanistic idea for how the target relates to the inputs. But a quick google search reveals that \"machine learning model\" is a common term so I'll just leave this as a comment.","created_at":"2018-08-14T06:32:43Z","updated_at":"2018-08-14T06:40:15Z","html_url":"https://github.com/qiime2/docs/pull/342#discussion_r209840747","pull_request_url":"https://api.github.com/repos/qiime2/docs/pulls/342","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/qiime2/docs/pulls/comments/209840747"},"html":{"href":"https://github.com/qiime2/docs/pull/342#discussion_r209840747"},"pull_request":{"href":"https://api.github.com/repos/qiime2/docs/pulls/342"}}},"pull_request":{"url":"https://api.github.com/repos/qiime2/docs/pulls/342","id":207389973,"node_id":"MDExOlB1bGxSZXF1ZXN0MjA3Mzg5OTcz","html_url":"https://github.com/qiime2/docs/pull/342","diff_url":"https://github.com/qiime2/docs/pull/342.diff","patch_url":"https://github.com/qiime2/docs/pull/342.patch","issue_url":"https://api.github.com/repos/qiime2/docs/issues/342","number":342,"state":"open","locked":false,"title":"ENH: additions to q2-sample-classifier tutorial, added pitfalls section","user":{"login":"nbokulich","id":1907564,"node_id":"MDQ6VXNlcjE5MDc1NjQ=","avatar_url":"https://avatars2.githubusercontent.com/u/1907564?v=4","gravatar_id":"","url":"https://api.github.com/users/nbokulich","html_url":"https://github.com/nbokulich","followers_url":"https://api.github.com/users/nbokulich/followers","following_url":"https://api.github.com/users/nbokulich/following{/other_user}","gists_url":"https://api.github.com/users/nbokulich/gists{/gist_id}","starred_url":"https://api.github.com/users/nbokulich/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nbokulich/subscriptions","organizations_url":"https://api.github.com/users/nbokulich/orgs","repos_url":"https://api.github.com/users/nbokulich/repos","events_url":"https://api.github.com/users/nbokulich/events{/privacy}","received_events_url":"https://api.github.com/users/nbokulich/received_events","type":"User","site_admin":false},"body":"This PR adds text modifications requested by @gregcaporaso after merging #333 (I have requested @gregcaporaso to review these sections)\r\n\r\nAlso adds some other tweaks to the text:\r\n1. links to scikit-learn documentation for more details on RFE, estimators, and feature importance calculations.\r\n2. 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