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 <[email protected]>
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 [email protected] or file a JIRA ticket
with INFRA.
---
