Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/13285#discussion_r67254109
 Diff: docs/sparkr.md 
@@ 285,71 +285,28 @@ 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 [spark.glm()](api/R/spark.glm.html)
+* Naive Bayes [spark.naiveBayes()](api/R/spark.naiveBayes.html)
+* KMeans [spark.kmeans()](api/R/spark.kmeans.html)
+* AFT Survival Regression [spark.survreg()](api/R/spark.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.
+[Generalized Linear Regression](api/R/spark.glm.html) can be used to train a model from
a 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.
+It produces the similar result compared with R summary function.
## Gaussian GLM model
+## Model persistence
<div datalang="r" markdown="1">
{% highlight r %}
# Create the DataFrame
df < createDataFrame(sqlContext, iris)

# Fit a gaussian GLM model over the dataset.
model < glm(Sepal_Length ~ Sepal_Width + Species, data = df, family = "gaussian")

# 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>
+* [write.ml](api/R/write.ml.html) allows users to save a fitted model in a given input
path
+* [read.ml](api/R/read.ml.html) allows users to read/load the model which was saved using
write.ml in a given path
## Binomial GLM model
+Model persistence is supported for all Machine Learning algorithms for all families.
<div datalang="r" markdown="1">
{% highlight r %}
# Create the DataFrame
df < createDataFrame(sqlContext, iris)
training < filter(df, df$Species != "setosa")

# Fit a binomial GLM model over the dataset.
model < glm(Species ~ Sepal_Length + Sepal_Width, data = training, family = "binomial")

# Model coefficients are returned in a similar format to R's native glm().
summary(model)
##$coefficients
## Estimate
##(Intercept) 13.046005
##Sepal_Length 1.902373
##Sepal_Width 0.404655
{% endhighlight %}
</div>
+The examples below show the use of building glm with Gaussian family,glm with Binomial
family, survreg, naiveBayes, kmeans models using SparkR
 End diff 
Organize better:
```
The examples below show how to build several models:
* GLM using the Gaussian and Binomial model families
* AFT survival regression model
* Naive Bayes model
* KMeans model
```

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