决策树和决策森林

概念

回归是预测一个数值型数量，比如大小，收入和温度。而分类是指预测标号或类别，比如判断邮件是否为“垃圾邮件”，拼图游戏的图案是否是“猫”。回归问题的目标为数值型特征，而分类问题的目标为类别型特征。

精确度是二元分类问题中一个常用的指标。精确度就是被标记为“正”而且确实是“正”的样本占所有标记为“正”的样本比例。召回率是被分类器标记为“正”的所有样本与所有本来就是“正”的样本的比率。

代码

object RDF {  def main(args: Array[String]) {    val sc = new SparkContext(new SparkConf().setAppName("RDF"))    val rawData = sc.textFile("/user/ds/covtype.data")    val data = rawData.map { line =>      val values = line.split(',').map(_.toDouble)      val featureVector = Vectors.dense(values.init)      val label = values.last - 1      LabeledPoint(label, featureVector)    }    val Array(trainData, cvData, testData) = data.randomSplit(Array(0.8, 0.1, 0.1))    trainData.cache()    cvData.cache()    testData.cache()    simpleDecisionTree(trainData, cvData)    randomClassifier(trainData, cvData)    evaluate(trainData, cvData, testData)    evaluateCategorical(rawData)    evaluateForest(rawData)    trainData.unpersist()    cvData.unpersist()    testData.unpersist()  }  def getMetrics(model: DecisionTreeModel, data: RDD[LabeledPoint]):  MulticlassMetrics = {    val predictionsAndLabels = data.map(example =>      (model.predict(example.features), example.label)    )    new MulticlassMetrics(predictionsAndLabels)  }  def simpleDecisionTree(trainData: RDD[LabeledPoint], cvData: RDD[LabeledPoint]): Unit = {    // Build a simple default DecisionTreeModel    val model = DecisionTree.trainClassifier(trainData, 7, Map[Int, Int](), "gini", 4, 100)    val metrics = getMetrics(model, cvData)    println(metrics.confusionMatrix)    println(metrics.precision)    (0 until 7) .map(     category  => (metrics.precision(category),metrics.recall(category))    ).foreach(println)  }  def classProbabilities(data:RDD[LabeledPoint]):Array[Double] = {    val countsByCategrory = data.map(_.label).countByValue()    val counts = countsByCategrory.toArray.sortBy(_._1).map(_._2)    counts.map(_.toDouble/counts.sum)  }  def randomClassifier(trainData: RDD[LabeledPoint], cvData: RDD[LabeledPoint]): Unit = {    val trainPriorProbabilities = classProbabilities(trainData)    val cvPriorProbabilities = classProbabilities(cvData)    val accuracy = trainPriorProbabilities.zip(cvPriorProbabilities).map {      case (trainProb, cvProb) => trainProb * cvProb    }.sum    println(accuracy)  }  def evaluate(              trainData:RDD[LabeledPoint],              cvData:RDD[LabeledPoint],              testData :RDD[LabeledPoint]              ):Unit = {   val evaluations =     for (impurity <- Array("gini","entropy");          depth <- Array(1,20);         bins <- Array(10,300))      yield {        val model = DecisionTree.trainClassifier(          trainData,7,Map[Int,Int](),impurity,depth,bins)        val accuracy = getMetrics(model,cvData).precision        ((impurity,depth,bins),accuracy)      }    evaluations.sortBy(_._2).reverse.foreach(println)    val model = DecisionTree.trainClassifier(      trainData.union(cvData), 7, Map[Int,Int](), "entropy", 20, 300)    println(getMetrics(model, testData).precision)    println(getMetrics(model, trainData.union(cvData)).precision)  }  def unencodeOneHot(rawData: RDD[String]): RDD[LabeledPoint] = {    rawData.map { line =>      val values = line.split(',').map(_.toDouble)      val wilderness = values.slice(10,14).indexOf(1.0).toDouble      val soil = values.slice(14,54).indexOf(1.0).toDouble      val featureVector = Vectors.dense(values.slice(0,10) :+ wilderness :+ soil)      val label = values.last -1      LabeledPoint(label,featureVector)    }  }  def evaluateCategorical(rawData: RDD[String]): Unit = {    val data = unencodeOneHot(rawData)    val Array(trainData,cvData,testData) = data.randomSplit(Array(0.8,0.1,0.1))    trainData.cache()    cvData.cache()    testData.cache()    val evaluations =      for (impurity <- Array("gini", "entropy");           depth    <- Array(10, 20, 30);           bins     <- Array(40, 300))        yield {          // Specify value count for categorical features 10, 11          val model = DecisionTree.trainClassifier(            trainData, 7, Map(10 -> 4, 11 -> 40), impurity, depth, bins)          val trainAccuracy = getMetrics(model, trainData).precision          val cvAccuracy = getMetrics(model, cvData).precision          // Return train and CV accuracy          ((impurity, depth, bins), (trainAccuracy, cvAccuracy))        }    evaluations.sortBy(_._2._2).reverse.foreach(println)    evaluations.sortBy(_._2._2).reverse.foreach(println)    val model = DecisionTree.trainClassifier(      trainData.union(cvData), 7, Map(10 -> 4, 11 -> 40), "entropy", 30, 300)    println(getMetrics(model, testData).precision)    trainData.unpersist()    cvData.unpersist()    testData.unpersist()  }  def evaluateForest(rawData: RDD[String]): Unit = {    val data = unencodeOneHot(rawData)    val Array(trainData, cvData) = data.randomSplit(Array(0.9, 0.1))    trainData.cache()    cvData.cache()    val forest = RandomForest.trainClassifier(      trainData, 7, Map(10 -> 4, 11 -> 40), 20, "auto", "entropy", 30, 300)    val predictionsAndLabels = cvData.map(example =>      (forest.predict(example.features), example.label)    )    println(new MulticlassMetrics(predictionsAndLabels).precision)    val input = "2709,125,28,67,23,3224,253,207,61,6094,0,29"    val vector = Vectors.dense(input.split(',').map(_.toDouble))    println(forest.predict(vector))  }}