GitHub user dbtsai opened a pull request: https://github.com/apache/spark/pull/1379
[SPARK-2309][MLlib] Generalize the binary logistic regression into multinomial logistic regression Currently, there is no multi-class classifier in mllib. Logistic regression can be extended to multinomial classifier straightforwardly. The following formula will be implemented. http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297/25 Note: When multi-classes mode, there will be multiple intercepts, so we don't use the single intercept in `GeneralizedLinearModel`, and have all the intercepts into weights. It makes some inconsistency. For example, in the binary mode, the intercept can not be specified by users, but since in the multinomial mode, the intercepts are combined into weights, users can specify them. @mengxr Should we just deprecate the intercept, and have everything in weights? It makes sense in term of optimization point of view, and also make the interface cleaner. Thanks. You can merge this pull request into a Git repository by running: $ git pull https://github.com/dbtsai/spark dbtsai-mlor Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/1379.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #1379 ---- commit 82dae74135bafa5d1adeef4b2b421693c05b2778 Author: DB Tsai <dbt...@alpinenow.com> Date: 2014-06-27T21:47:15Z Multinomial Logistic Regression ---- --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. ---