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author | GayathriMurali <gayathri.m@intel.com> | 2016-06-17 21:10:29 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2016-06-17 21:10:29 -0700 |
commit | af2a4b0826b2358c0fe75c3e4d7fd8f7bccdd8e5 (patch) | |
tree | b251b61bb5e19bce2345e9846b975fd2a25e0e3f /docs/sparkr.md | |
parent | 10b671447bc04af250cbd8a7ea86f2769147a78a (diff) | |
download | spark-af2a4b0826b2358c0fe75c3e4d7fd8f7bccdd8e5.tar.gz spark-af2a4b0826b2358c0fe75c3e4d7fd8f7bccdd8e5.tar.bz2 spark-af2a4b0826b2358c0fe75c3e4d7fd8f7bccdd8e5.zip |
[SPARK-15129][R][DOC] R API changes in ML
## What changes were proposed in this pull request?
Make user guide changes to SparkR documentation for all changes that happened in 2.0 to Machine Learning APIs
Author: GayathriMurali <gayathri.m@intel.com>
Closes #13285 from GayathriMurali/SPARK-15129.
Diffstat (limited to 'docs/sparkr.md')
-rw-r--r-- | docs/sparkr.md | 77 |
1 files changed, 19 insertions, 58 deletions
diff --git a/docs/sparkr.md b/docs/sparkr.md index 59e486d192..961bd323fa 100644 --- a/docs/sparkr.md +++ b/docs/sparkr.md @@ -285,71 +285,32 @@ 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 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](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 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](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 data-lang="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 how to build several models: +* GLM using the Gaussian and Binomial model families +* AFT survival regression model +* Naive Bayes model +* K-Means model + +{% include_example r/ml.R %} # R Function Name Conflicts |