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https://issues.apache.org/jira/browse/SPARK-12811?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yanbo Liang updated SPARK-12811:
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Comment: was deleted
(was: 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.
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