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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