{"action":"created","comment":{"url":"https://api.github.com/repos/apache/incubator-madlib/pulls/comments/68993568","id":68993568,"diff_hunk":"@@ -676,27 +985,32 @@ def svm_help(schema_madlib, message, is_svc, **kwargs):\n def svm(schema_madlib, source_table, model_table,\n         dependent_varname, independent_varname, kernel_func,\n         kernel_params, grouping_col, params, is_svc,\n-        verbose, detect_novelty=False, **kwargs):\n+        verbose, add_intercept=False, **kwargs):","path":"src/ports/postgres/modules/svm/svm.py_in","position":612,"original_position":612,"commit_id":"9d244e945c31d1e3b0cc9781596ffacc97b85eb0","original_commit_id":"9d244e945c31d1e3b0cc9781596ffacc97b85eb0","user":{"login":"mktal","id":4022529,"avatar_url":"https://avatars.githubusercontent.com/u/4022529?v=3","gravatar_id":"","url":"https://api.github.com/users/mktal","html_url":"https://github.com/mktal","followers_url":"https://api.github.com/users/mktal/followers","following_url":"https://api.github.com/users/mktal/following{/other_user}","gists_url":"https://api.github.com/users/mktal/gists{/gist_id}","starred_url":"https://api.github.com/users/mktal/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mktal/subscriptions","organizations_url":"https://api.github.com/users/mktal/orgs","repos_url":"https://api.github.com/users/mktal/repos","events_url":"https://api.github.com/users/mktal/events{/privacy}","received_events_url":"https://api.github.com/users/mktal/received_events","type":"User","site_admin":false},"body":"It occurs to me that `add_intercept` will always be `False` here since we are not exposing it to the users and no client code is calling this function. A relevant question to think about is whether we should add `fit_intercept` to the `params` and discourage the use of `array[..., 1]`.   ","created_at":"2016-06-29T17:44:43Z","updated_at":"2016-06-29T17:44:43Z","html_url":"https://github.com/apache/incubator-madlib/pull/48#discussion_r68993568","pull_request_url":"https://api.github.com/repos/apache/incubator-madlib/pulls/48","_links":{"self":{"href":"https://api.github.com/repos/apache/incubator-madlib/pulls/comments/68993568"},"html":{"href":"https://github.com/apache/incubator-madlib/pull/48#discussion_r68993568"},"pull_request":{"href":"https://api.github.com/repos/apache/incubator-madlib/pulls/48"}}},"pull_request":{"url":"https://api.github.com/repos/apache/incubator-madlib/pulls/48","id":74306027,"html_url":"https://github.com/apache/incubator-madlib/pull/48","diff_url":"https://github.com/apache/incubator-madlib/pull/48.diff","patch_url":"https://github.com/apache/incubator-madlib/pull/48.patch","issue_url":"https://api.github.com/repos/apache/incubator-madlib/issues/48","number":48,"state":"open","locked":false,"title":"SVM: Novelty detection using 1-class SVM","user":{"login":"iyerr3","id":2237447,"avatar_url":"https://avatars.githubusercontent.com/u/2237447?v=3","gravatar_id":"","url":"https://api.github.com/users/iyerr3","html_url":"https://github.com/iyerr3","followers_url":"https://api.github.com/users/iyerr3/followers","following_url":"https://api.github.com/users/iyerr3/following{/other_user}","gists_url":"https://api.github.com/users/iyerr3/gists{/gist_id}","starred_url":"https://api.github.com/users/iyerr3/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/iyerr3/subscriptions","organizations_url":"https://api.github.com/users/iyerr3/orgs","repos_url":"https://api.github.com/users/iyerr3/repos","events_url":"https://api.github.com/users/iyerr3/events{/privacy}","received_events_url":"https://api.github.com/users/iyerr3/received_events","type":"User","site_admin":false},"body":"Jira: MADLIB-990\r\n\r\nAdditional author: Nandish Jayaram <njayaram@pivotal.io>\r\n\r\nIn this implementation of a one-class SVM, we are piggy-backing on the existing\r\nSVM classification. The input table to a one-class SVM does not require a\r\ndependent variable. A maximum-margin classifier is learned that separates all\r\nthe data from the origin. The default kernel for one-class is Gaussian 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