Mastering Machine Learning with Spark 2.x

Random forest model

Now, let's try building a random forest using 10 decision trees.

val numClasses = 2 
val categoricalFeaturesInfo = Map[Int, Int]() 
val numTrees = 10 
val featureSubsetStrategy = "auto"  
val impurity = "gini" 
val maxDepth = 5 
val maxBins = 10 
val seed = 42 
 
 
val rfModel = RandomForest.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo, 
  numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed) 
 

Just like our single decision tree model, we start by declaring the hyper-parameters, many of which should be familiar to you already from the decision tree example. In the preceding code, we will start by creating a random forest of 10 trees, solving a two-class problem. One key feature that is different is the ...