logistic迴歸又稱logistic迴歸分析，是一種廣義的線性迴歸分析模型，常用於資料探勘，疾病自動診斷，經濟預測等領域.以下是spark中該演算法的實現方式，原地址為http://spark.apache.org/docs/latest/mllib-linear-methods.html#classification

package alg

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
import
 
 org.apache.spark.mllib.evaluation.MulticlassMetrics
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils

object logisticRegression {

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

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

    //1.載入資料
    val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")

    //2.劃分訓練集和測試集
    val splitData=data.randomSplit(Array(0.6,0.4),seed=11L)

    val training=splitData(0).cache()

    val test=splitData(1)

    val model=new LogisticRegressionWithLBFGS
 
()
      .setNumClasses(10)
      .run(training)

    //4.在測試集上驗證
    val predictionAndLabels = test.map { case LabeledPoint(label, features) =>
      val prediction = model.predict(features)
      (prediction, label)
    }

    val metrics=new MulticlassMetrics(predictionAndLabels)

    val accuracy=metrics.accuracy

    println(s"Accuracy=$accuracy")

    model.save(sc, "target/tmp/scalaLogisticRegressionWithLBFGSModel")
    
  }

}