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https://issues.apache.org/jira/browse/SPARK-2309?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14950208#comment-14950208
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christian sommeregger commented on SPARK-2309:
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Hey everybody!
After inspecting the code on github I believe that we have not really
implemented the standard multinomial problem from
http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297/25
but a model that covers a set of binary choices with item specific weights,
which is a slightly different thing.
For a true multinomial setup each row in the training data needs to containt
all items (K = number of choices) that were available in a specific choice
situation,
The current labelled point object however has just a choice flag + the
respective features of one item in each row:
e.g.: Labelled point (K=1)
0 | (,,,,)
1 | (,,,,)
3 | (,,,,)
3 | (,,,,)
0 | (,,,,)
....
For the model on http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297/25 we
would rather need the following structure.
e.g.: Always three Items in the choice set (K=3)
Choice Indicator | Item1Features | Item2Features | Item3Features
1 | (,,,,) | (,,,,) | (,,,,)
3 | (,,,,) | (,,,,) | (,,,,)*
3 | (,,,,) | (,,,,) | (,,,,)*
....
e.g.: Flexible number of Items in the choice set (K varies)
8 | (,,,,) | (,,,,) | (,,,,) | (,,,,) | (,,,,) | (,,,,) | (,,,,) |
(,,,,)* | (,,,,)
2 | (,,,,) | (,,,,)* | (,,,,)
3 | (,,,,) | (,,,,) | (,,,,)* | (,,,,)
> 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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