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

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

Xiangrui Meng created SPARK-12811:
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             Summary: 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


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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