在spark2.0以上版本中，存在兩種對機器學習演算法的實現庫MLlib與ML，比如隨機森林：
org.apache.spark.mllib.tree.RandomForest
和
org.apache.spark.ml.classification.RandomForestClassificationModel

兩種庫對應的使用方法也不同，Mllib是RDD-based API，
ML是基於ML pipeline的API與dataframe的資料結構。
詳見http://spark.apache.org/docs/latest/ml-guide.html
所以官方例項也是有很大區別的，下面分別給出了原始碼和註釋：

MLlib的模型實現

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.mllib.util.MLUtils
// $example off$

object RandomForestClassificationExample {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("RandomForestClassificationExample")
    val sc = new SparkContext(conf)
    // $example on$
    // Load and parse the data file.
    val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
    // Split the data into training and test sets (30% held out for testing)
    val splits = data.randomSplit(Array(0.7, 0.3))
    val (trainingData, testData) = (splits(0), splits(1))

    // Train a RandomForest model.
    // Empty categoricalFeaturesInfo indicates all features are continuous.
    val numClasses = 2
    val categoricalFeaturesInfo = Map[Int, Int]()
    val numTrees = 3 // Use more in practice.
    val featureSubsetStrategy = "auto" // Let the algorithm choose.
    val impurity = "gini"
    val maxDepth = 4
    val maxBins = 32

    val model = RandomForest.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo,
      numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)

    // Evaluate model on test instances and compute test error
    val labelAndPreds = testData.map { point =>
      val prediction = model.predict(point.features)
      (point.label, prediction)
    }
    val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count()
    println("Test Error = " + testErr)
    println("Learned classification forest model:\n" + model.toDebugString)

    // Save and load model
    model.save(sc, "target/tmp/myRandomForestClassificationModel")
    val sameModel = RandomForestModel.load(sc, "target/tmp/myRandomForestClassificationModel")
    // $example off$
  }
}
// scalastyle:on println
 

ML的模型實現

// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{RandomForestClassificationModel, RandomForestClassifier}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
// $example off$
import org.apache.spark.sql.SparkSession

object RandomForestClassifierExample {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession
      .builder
      .appName("RandomForestClassifierExample")
      .getOrCreate()

    // $example on$
    // Load and parse the data file, converting it to a DataFrame.
    val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")

    // Index labels, adding metadata to the label column.
    // Fit on whole dataset to include all labels in index.
    val labelIndexer = new StringIndexer()
      .setInputCol("label")
      .setOutputCol("indexedLabel")
      .fit(data)
    // Automatically identify categorical features, and index them.
    // Set maxCategories so features with > 4 distinct values are treated as continuous.
    val featureIndexer = new VectorIndexer()
      .setInputCol("features")
      .setOutputCol("indexedFeatures")
      .setMaxCategories(4)
      .fit(data)

    // Split the data into training and test sets (30% held out for testing).
    val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

    // Train a RandomForest model.
    val rf = new RandomForestClassifier()
      .setLabelCol("indexedLabel")
      .setFeaturesCol("indexedFeatures")
      .setNumTrees(10)

    // Convert indexed labels back to original labels.
    val labelConverter = new IndexToString()
      .setInputCol("prediction")
      .setOutputCol("predictedLabel")
      .setLabels(labelIndexer.labels)

    // Chain indexers and forest in a Pipeline.
    val pipeline = new Pipeline()
      .setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))

    // Train model. This also runs the indexers.
    val model = pipeline.fit(trainingData)

    // Make predictions.
    val predictions = model.transform(testData)

    // Select example rows to display.
    predictions.select("predictedLabel", "label", "features").show(5)

    // Select (prediction, true label) and compute test error.
    val evaluator = new MulticlassClassificationEvaluator()
      .setLabelCol("indexedLabel")
      .setPredictionCol("prediction")
      .setMetricName("accuracy")
    val accuracy = evaluator.evaluate(predictions)
    println("Test Error = " + (1.0 - accuracy))

    val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
    println("Learned classification forest model:\n" + rfModel.toDebugString)
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println
 

TIPS：
想看http://spark.apache.org/docs裡面示例程式碼的全部嗎？一種方法是去github上找，另一種方法是進spark的安裝目錄，所有的原始碼都在 spark/examples/src/main/scala/裡面，
如ML的演算法scala實現：
spark/examples/src/main/scala/org/apache/spark/examples/ml
MLlib的演算法scala實現：
spark/examples/src/main/scala/org/apache/spark/examples/mllib