Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/13285#discussion_r65202390
 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 pvalues. (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 ''.
The examples below show the use of building gaussian GLM model and binomial GLM model
using SparkR.
+The [summary()](api/R/summary.html) function gives the summary of a model produced by
different algorithms listed above.
+This summary is same as the result of summary() function in R.
## Gaussian GLM model
+## Model persistence
<div datalang="r" markdown="1">
{% highlight r %}
# Create the DataFrame
df < createDataFrame(sqlContext, iris)
+* write.ml allows users to save a fitted model in a given input path
+* read.ml allows users to read/load the model which was saved using write.ml
+
+Model persistence is supported for all Machine Learning algorithms for all families.
# Fit a gaussian GLM model over the dataset.
model < glm(Sepal_Length ~ Sepal_Width + Species, data = df, family = "gaussian")
+The examples below show the use of building Gaussian GLM, NaiveBayes, kMeans and AFTSurvivalReg
using SparkR
+{% include_example r/ml.r %}
+
+# GLM Summary() Result
+
+Here is an example of the output from the summary() function for GLM
+
+{% highlight r %}
# Model summary are returned in a similar format to R's native glm().
summary(model)
##$devianceResiduals
## Min Max
## 1.307112 1.412532
##
##$coefficients
## Estimate Std. Error t value Pr(>t)
##(Intercept) 2.251393 0.3697543 6.08889 9.568102e09
##Sepal_Width 0.8035609 0.106339 7.556598 4.187317e12
##Species_versicolor 1.458743 0.1121079 13.01195 0
##Species_virginica 1.946817 0.100015 19.46525 0

# Make predictions based on the model.
predictions < predict(model, newData = df)
head(select(predictions, "Sepal_Length", "prediction"))
## Sepal_Length prediction
##1 5.1 5.063856
##2 4.9 4.662076
##3 4.7 4.822788
##4 4.6 4.742432
##5 5.0 5.144212
##6 5.4 5.385281
{% endhighlight %}
</div>
+##Deviance Residuals:
 End diff 
Since it's different to maintain the summary output, since we may update the included
examples. I vote to just remove the summary output section.

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