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https://issues.apache.org/jira/browse/SPARK-12566?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15229368#comment-15229368
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Joseph K. Bradley edited comment on SPARK-12566 at 4/6/16 11:47 PM:
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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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