Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/13285#discussion_r65200612
--- Diff: docs/sparkr.md ---
@@ -285,71 +285,57 @@ head(teenagers)
# Machine Learning
-SparkR allows the fitting of generalized linear models over DataFrames
using the [glm()](api/R/glm.html) function. Under the hood, SparkR uses MLlib
to train a model of the specified family. Currently the gaussian and binomial
families are supported. We support a subset of the available R formula
operators for model fitting, including '~', '.', ':', '+', and '-'.
+SparkR supports the following Machine Learning algorithms.
-The [summary()](api/R/summary.html) function gives the summary of a model
produced by [glm()](api/R/glm.html).
+* Generalized Linear Regression Model [glm()](api/R/glm.html)
+* Naive Bayes [naiveBayes()](api/R/naiveBayes.html)
+* KMeans [kmeans()](api/R/kmeans.html)
+* AFT Survival Regression [survreg()](api/R/survreg.html)
-* For gaussian GLM model, it returns a list with 'devianceResiduals' and
'coefficients' components. The 'devianceResiduals' gives the min/max deviance
residuals of the estimation; the 'coefficients' gives the estimated
coefficients and their estimated standard errors, t values and p-values. (It
only available when model fitted by normal solver.)
-* For binomial GLM model, it returns a list with 'coefficients' component
which gives the estimated coefficients.
+Under the hood, SparkR uses MLlib to train a model of the specified
family. Currently the gaussian, binomial, Poisson and Gamma families are
supported. We support a subset of the available R formula operators for model
fitting, including '~', '.', ':', '+', and '-'.
--- End diff --
SparkR supports 4 MLlib machine learning models, the description here is
only applicable to ```spark.glm```.
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