SVM(Support Vector Machine)指的是支援向量機，是常見的一種判別方法。在機器學習領域，是一個有監督的學習模型，通常用來進行模式識別、分類以及迴歸分析。下面是spark官網給出的例子。原網址為http://spark.apache.org/docs/latest/mllib-linear-methods.html#classification

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.classification.{SVMModel, SVMWithSGD}
 

import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.util.MLUtils
object spark_svm {

  def main(args :Array[String]): Unit = {

    val sparkConf = new SparkConf().setMaster("local").setAppName("testTansformition")
    val sc = new SparkContext(sparkConf)
 


    //載入訓練資料　LIBSVＭ資料格式.
    val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")

    // 劃分訓練集和測試機集（訓練集６０％，測試集４０％）
    val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
    val training = splits(0).cache()
    val test = splits(1)

    // 訓練模型
    val numIterations = 100
    val model =
 
 SVMWithSGD.train(training, numIterations)

    // 清楚預設閾值
    model.clearThreshold()

    // 對測試集進行預測
    val scoreAndLabels = test.map { point =>
      val score = model.predict(point.features)
      (score, point.label)
    }

    //獲取評價指標
    val metrics = new BinaryClassificationMetrics(scoreAndLabels)
    val auROC = metrics.areaUnderROC()

    println(s"Area under ROC = $auROC")

    // 儲存和載入模型示例
    model.save(sc, "target/tmp/scalaSVMWithSGDModel")
    val sameModel = SVMModel.load(sc, "target/tmp/scalaSVMWithSGDModel")

    Thread.sleep(30*30*1000);

  }

}