Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/16148#discussion_r91201507
--- Diff: docs/sparkr.md ---
@@ -512,39 +512,33 @@ head(teenagers)
# Machine Learning
-SparkR supports the following machine learning algorithms currently:
`Generalized Linear Model`, `Accelerated Failure Time (AFT) Survival Regression
Model`, `Naive Bayes Model` and `KMeans Model`.
-Under the hood, SparkR uses MLlib to train the model.
-Users can call `summary` to print a summary of the fitted model,
[predict](api/R/predict.html) to make predictions on new data, and
[write.ml](api/R/write.ml.html)/[read.ml](api/R/read.ml.html) to save/load
fitted models.
-SparkR supports a subset of the available R formula operators for model
fitting, including â~â, â.â, â:â, â+â, and â-â.
-
## Algorithms
-### Generalized Linear Model
-
-[spark.glm()](api/R/spark.glm.html) or [glm()](api/R/glm.html) fits
generalized linear model against a Spark DataFrame.
-Currently "gaussian", "binomial", "poisson" and "gamma" families are
supported.
--- End diff --
Ok, generally I'd agree. I think we should have more information on this
though since the SparkR API doc is still kind of thin, perhaps this should be
part of the JIRA on R content for the ML programming guide.
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