Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/13285#discussion_r65822439
--- 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/glm.html)
+* Naive Bayes [spark.naiveBayes()](api/R/naiveBayes.html)
+* KMeans [spark.kmeans()](api/R/kmeans.html)
+* AFT Survival Regression [spark.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.
+Generalized Linear Regression 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.
+This summary is same as the result of summary() function in R.
-## Gaussian GLM model
+## Model persistence
-<div data-lang="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 9.568102e-09
-##Sepal_Width 0.8035609 0.106339 7.556598 4.187317e-12
-##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 allows users to save a fitted model in a given input path
--- End diff --
```[write.ml](api/R/write.ml.html)``` and ditto for ```read.ml```.
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