Regression trees

We have seen classification trees in the previous chapter. One can build a recursive split-and-concur structure for a regression problem, where a split is chosen to minimize the remaining variance. Regression trees are less popular than decision trees or classical ANOVA analysis; however, let's provide an example of a regression tree here as a part of MLlib:

akozlov@Alexanders-MacBook-Pro$ bin/spark-shell 
Welcome to
      ____              __
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    _ / _ / _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_   version 1.6.1-SNAPSHOT
      /_/

Using Scala version 2.10.5 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_40)
Type in expressions to have them evaluated.
Type :help for more information.
Spark context available as sc.
SQL context available as sqlContext.

scala> import org.apache.spark.mllib.tree.DecisionTree
import org.apache.spark.mllib.tree.DecisionTree

scala> import org.apache.spark.mllib.tree.model.DecisionTreeModel
import org.apache.spark.mllib.tree.model.DecisionTreeModel

scala> import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.util.MLUtils

scala> // Load and parse the data file.

scala> val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[6] at map at MLUtils.scala:112

scala> // Split the data into training and test sets (30% held out for testing)

scala> val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
trainingData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[7] at randomSplit at <console>:26
testData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[8] at randomSplit at <console>:26

scala> val categoricalFeaturesInfo = Map[Int, Int]()
categoricalFeaturesInfo: scala.collection.immutable.Map[Int,Int] = Map()

scala> val impurity = "variance"
impurity: String = variance

scala> val maxDepth = 5
maxDepth: Int = 5

scala> val maxBins = 32
maxBins: Int = 32

scala> val model = DecisionTree.trainRegressor(trainingData, categoricalFeaturesInfo, impurity, maxDepth, maxBins)
model: org.apache.spark.mllib.tree.model.DecisionTreeModel = DecisionTreeModel regressor of depth 2 with 5 nodes

scala> val labelsAndPredictions = testData.map { point =>
     |   val prediction = model.predict(point.features)
     |   (point.label, prediction)
     | }
labelsAndPredictions: org.apache.spark.rdd.RDD[(Double, Double)] = MapPartitionsRDD[20] at map at <console>:36

scala> val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean()
testMSE: Double = 0.07407407407407407

scala> println(s"Test Mean Squared Error = $testMSE")
Test Mean Squared Error = 0.07407407407407407

scala> println("Learned regression tree model:
" + model.toDebugString)
Learned regression tree model:
DecisionTreeModel regressor of depth 2 with 5 nodes
  If (feature 378 <= 71.0)
   If (feature 100 <= 165.0)
    Predict: 0.0
   Else (feature 100 > 165.0)
    Predict: 1.0
  Else (feature 378 > 71.0)
   Predict: 1.0

The splits at each level are made to minimize the variance, as follows:

Regression trees

which is equivalent to minimizing the Regression trees distances between the label values and their mean within each leaf summed over all the leaves of the node.

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