[SPARK-20849][DOC][SPARKR] Document R DecisionTree

R/pkg/vignettes/sparkr-vignettes.Rmd

@@ -503,6 +503,8 @@ SparkR supports the following machine learning models and algorithms.
 
 #### Tree - Classification and Regression
 
+* Decision Tree
+
 * Gradient-Boosted Trees (GBT)
 
 * Random Forest
@@ -776,16 +778,32 @@ newDF <- createDataFrame(data.frame(x = c(1.5, 3.2)))
 head(predict(isoregModel, newDF))
 ```
 
+#### Decision Tree
+
+`spark.decisionTree` fits a [decision tree](https://en.wikipedia.org/wiki/Decision_tree_learning) classification or regression model on a `SparkDataFrame`.
+Users can call `summary` to get a summary of the fitted model, `predict` to make predictions, and `write.ml`/`read.ml` to save/load fitted models.
+
+We use the `Titanic` dataset to train a decision tree and make predictions:
+
+```{r}
+t <- as.data.frame(Titanic)
+df <- createDataFrame(t)
+dtModel <- spark.decisionTree(df, Survived ~ ., type = "classification", maxDepth = 2)
+summary(dtModel)
+predictions <- predict(dtModel, df)
+```
+
 #### Gradient-Boosted Trees
 
 `spark.gbt` fits a [gradient-boosted tree](https://en.wikipedia.org/wiki/Gradient_boosting) classification or regression model on a `SparkDataFrame`.
 Users can call `summary` to get a summary of the fitted model, `predict` to make predictions, and `write.ml`/`read.ml` to save/load fitted models.
 
-We use the `longley` dataset to train a gradient-boosted tree and make predictions:
+We use the `Titanic` dataset to train a gradient-boosted tree and make predictions:
 
-```{r, warning=FALSE}
-df <- createDataFrame(longley)
-gbtModel <- spark.gbt(df, Employed ~ ., type = "regression", maxDepth = 2, maxIter = 2)
+```{r}
+t <- as.data.frame(Titanic)
+df <- createDataFrame(t)
+gbtModel <- spark.gbt(df, Survived ~ ., type = "classification", maxDepth = 2, maxIter = 2)
 summary(gbtModel)
 predictions <- predict(gbtModel, df)
 ```
@@ -795,11 +813,12 @@ predictions <- predict(gbtModel, df)
 `spark.randomForest` fits a [random forest](https://en.wikipedia.org/wiki/Random_forest) classification or regression model on a `SparkDataFrame`.
 Users can call `summary` to get a summary of the fitted model, `predict` to make predictions, and `write.ml`/`read.ml` to save/load fitted models.
 
-In the following example, we use the `longley` dataset to train a random forest and make predictions:
+In the following example, we use the `Titanic` dataset to train a random forest and make predictions:
 
-```{r, warning=FALSE}
-df <- createDataFrame(longley)
-rfModel <- spark.randomForest(df, Employed ~ ., type = "regression", maxDepth = 2, numTrees = 2)
+```{r}
+t <- as.data.frame(Titanic)
+df <- createDataFrame(t)
+rfModel <- spark.randomForest(df, Survived ~ ., type = "classification", maxDepth = 2, numTrees = 2)
 summary(rfModel)
 predictions <- predict(rfModel, df)
 ```
@@ -965,17 +984,18 @@ Given a `SparkDataFrame`, the test compares continuous data in a given column `t
 specified by parameter `nullHypothesis`.
 Users can call `summary` to get a summary of the test results.
 
-In the following example, we test whether the `longley` dataset's `Armed_Forces` column
+In the following example, we test whether the `Titanic` dataset's `Freq` column
 follows a normal distribution.  We set the parameters of the normal distribution using
 the mean and standard deviation of the sample.
 
-```{r, warning=FALSE}
-df <- createDataFrame(longley)
-afStats <- head(select(df, mean(df$Armed_Forces), sd(df$Armed_Forces)))
-afMean <- afStats[1]
-afStd <- afStats[2]
+```{r}
+t <- as.data.frame(Titanic)
+df <- createDataFrame(t)
+freqStats <- head(select(df, mean(df$Freq), sd(df$Freq)))
+freqMean <- freqStats[1]
+freqStd <- freqStats[2]
 
-test <- spark.kstest(df, "Armed_Forces", "norm", c(afMean, afStd))
+test <- spark.kstest(df, "Freq", "norm", c(freqMean, freqStd))
 testSummary <- summary(test)
 testSummary
 ```

docs/ml-classification-regression.md

@@ -708,6 +708,13 @@ More details on parameters can be found in the [Python API documentation](api/py
 {% include_example python/ml/decision_tree_regression_example.py %}
 </div>
 
+<div data-lang="r" markdown="1">
+
+Refer to the [R API docs](api/R/spark.decisionTree.html) for more details.
+
+{% include_example regression r/ml/decisionTree.R %}
+</div>
+
 </div>
 
 

docs/sparkr.md

@@ -492,6 +492,7 @@ SparkR supports the following machine learning algorithms currently:
 
 #### Tree
 
+* [`spark.decisionTree`](api/R/spark.decisionTree.html): `Decision Tree for` [`Regression`](ml-classification-regression.html#decision-tree-regression) `and` [`Classification`](ml-classification-regression.html#decision-tree-classifier)
 * [`spark.gbt`](api/R/spark.gbt.html): `Gradient Boosted Trees for` [`Regression`](ml-classification-regression.html#gradient-boosted-tree-regression) `and` [`Classification`](ml-classification-regression.html#gradient-boosted-tree-classifier)
 * [`spark.randomForest`](api/R/spark.randomForest.html): `Random Forest for` [`Regression`](ml-classification-regression.html#random-forest-regression) `and` [`Classification`](ml-classification-regression.html#random-forest-classifier)
 

examples/src/main/r/ml/decisionTree.R

@@ -0,0 +1,65 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/decisionTree.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-decisionTree-example")
+
+# DecisionTree classification model
+
+# $example on:classification$
+# Load training data
+df <- read.df("data/mllib/sample_libsvm_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# Fit a DecisionTree classification model with spark.decisionTree
+model <- spark.decisionTree(training, label ~ features, "classification")
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+head(predictions)
+# $example off:classification$
+
+# DecisionTree regression model
+
+# $example on:regression$
+# Load training data
+df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# Fit a DecisionTree regression model with spark.decisionTree
+model <- spark.decisionTree(training, label ~ features, "regression")
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+head(predictions)
+# $example off:regression$
+
+sparkR.session.stop()