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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (SPARK-12566) GLM model family, link function support in SparkR:::glm
Date Wed, 06 Apr 2016 23:48:25 GMT

    [ https://issues.apache.org/jira/browse/SPARK-12566?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15229368#comment-15229368
] 

Joseph K. Bradley edited comment on SPARK-12566 at 4/6/16 11:47 PM:
--------------------------------------------------------------------

Here's my preferred design.  I prefer to abstract the implementation (solver) from the API
(model) as much as possible.
* R glm calls Scala GLM, using solver = auto by default
* Scala GLM has solver = auto by default.  Auto should mean "best effort"
** With few features (< 4K or so),
*** For family = gaussian and link = identity, use normal equations.
*** For others, use IRLS.
** With many features, use LBFGS if possible (for family, link).  Otherwise, throw an exception.
* Scala LinearRegression, LogisticRegression call GLM.  I.e., they uses normal equations,
IRLS when possible.

What do yall think?

(update: now catching up on PR comments...)


was (Author: josephkb):
Here's my preferred design.  I prefer to abstract the implementation (solver) from the API
(model) as much as possible.
* R glm calls Scala GLM, using solver = auto by default
* Scala GLM has solver = auto by default.  Auto should mean "best effort"
** With few features (< 4K or so),
*** For family = gaussian and link = identity, use normal equations.
*** For others, use IRLS.
** With many features, use LBFGS if possible (for family, link).  Otherwise, throw an exception.
* Scala LinearRegression, LogisticRegression call GLM.  I.e., they uses normal equations,
IRLS when possible.

What do yall think?

> GLM model family, link function support in SparkR:::glm
> -------------------------------------------------------
>
>                 Key: SPARK-12566
>                 URL: https://issues.apache.org/jira/browse/SPARK-12566
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML, SparkR
>            Reporter: Joseph K. Bradley
>            Assignee: yuhao yang
>            Priority: Critical
>
> This JIRA is for extending the support of MLlib's Generalized Linear Models (GLMs) to
more model families and link functions in SparkR. After SPARK-12811, we should be able to
wrap GeneralizedLinearRegression in SparkR with support of popular families and link functions.



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