Github user yanboliang commented on a diff in the pull request: https://github.com/apache/spark/pull/16148#discussion_r91022027 --- 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 -- These descriptions can be found in the SparkR API doc. I'm more prefer to link the algorithms listed here to the corresponding R API docs and MLlib user guide sections rather than duplicated adding them here.
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