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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-2309) Generalize the binary logistic regression into multinomial logistic regression
Date Sat, 10 Oct 2015 09:26:05 GMT

    [ https://issues.apache.org/jira/browse/SPARK-2309?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14951744#comment-14951744
] 

Sean Owen commented on SPARK-2309:
----------------------------------

Yeah, I also don't get it. In multinomial LR you still have the same features for every output
class. The slide you show just shows a loss function computed over the loss for each of the
N classes, not just 1. But the features are the same. Implicitly, if an example is in class
k then it's not in the other classes.

> Generalize the binary logistic regression into multinomial logistic regression
> ------------------------------------------------------------------------------
>
>                 Key: SPARK-2309
>                 URL: https://issues.apache.org/jira/browse/SPARK-2309
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: DB Tsai
>            Assignee: DB Tsai
>            Priority: Critical
>             Fix For: 1.3.0
>
>
> Currently, there is no multi-class classifier in mllib. Logistic regression can be extended
to multinomial one straightforwardly. 
> The following formula will be implemented. 
> http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297/25



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