[jira] [Commented] (SPARK-12811) Estimator interface for generalized linear models (GLMs)

["Yanbo Liang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-12811?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15121176#comment-15121176 ] 

Yanbo Liang commented on SPARK-12811:
-------------------------------------

Should we put it under a new folder named "ml/glm"?

> Estimator interface for generalized linear models (GLMs)
> --------------------------------------------------------
>
>                 Key: SPARK-12811
>                 URL: https://issues.apache.org/jira/browse/SPARK-12811
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML
>    Affects Versions: 2.0.0
>            Reporter: Xiangrui Meng
>            Assignee: Yanbo Liang
>            Priority: Critical
>
> In Spark 1.6, MLlib provides logistic regression and linear regression with L1/L2/elastic-net regularization. We want to expand the support of generalized linear models (GLMs) in 2.0, e.g., Poisson/Gamma families and more link functions. SPARK-9835 implements a GLM solver for the case when the number of features is small. We also need to design an interface for GLMs.
> In SparkR, we can simply follow glm or glmnet. On the Python/Scala/Java side, the interface should be consistent with LinearRegression and LogisticRegression, e.g.,
> {code}
> val glm = new GeneralizedLinearModel()
>   .setFamily("poisson")
>   .setSolver("irls")
> {code}
> It would be great if LinearRegression and LogisticRegression can reuse code from GeneralizedLinearModel.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org