Spark中组件Mllib的学习27之逻辑回归-多元逻辑回归，较大数据集，带预测准确度计算

更多代码请见：https://github.com/xubo245/SparkLearning
Spark中组件Mllib的学习之逻辑回归篇
1解释
但预测较多数据集，需要去计算准确度

2.代码：

/**  * @author xubo  *         ref:Spark MlLib机器学习实战  *         more code:https://github.com/xubo245/SparkLearning  *         more blog:http://blog.csdn.net/xubo245  */package org.apache.spark.mllib.learning.regressionimport org.apache.spark.mllib.classification.LogisticRegressionWithSGDimport org.apache.spark.mllib.evaluation.MulticlassMetricsimport org.apache.spark.mllib.regression.LabeledPointimport org.apache.spark.mllib.util.MLUtilsimport org.apache.spark.{SparkConf, SparkContext}/**  * Created by xubo on 2016/5/23.  * 多元逻辑回归,带验证  */object LogisticRegression3Learning {  def main(args: Array[String]) {    val conf = new SparkConf().setMaster("local[4]").setAppName(this.getClass().getSimpleName().filter(!_.equals('$')))    val sc = new SparkContext(conf)    val data = MLUtils.loadLibSVMFile(sc, "file/data/mllib/input/regression/sample_libsvm_data.txt") //读取数据文件    val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L) //对数据集切分    val parsedData = splits(0) //分割训练数据    val parseTtest = splits(1) //分割测试数据    val model = LogisticRegressionWithSGD.train(parsedData, 50) //训练模型    println(model.weights) //打印θ值    println("model.weights.size:" + model.weights.size) //打印θ数量    val predictionAndLabels = parseTtest.map {        //计算测试值        case LabeledPoint(label, features) => //计算测试值          val prediction = model.predict(features) //计算测试值          (prediction, label) //存储测试和预测值      }    val metrics = new MulticlassMetrics(predictionAndLabels) //创建验证类    val precision = metrics.precision //计算验证值    println("data:" + data.count())    println("parsedData:" + parsedData.count())    println("parseTtest:" + parseTtest.count())    println("Precision = " + precision) //打印验证值    predictionAndLabels.take(10).foreach(println)    sc.stop  }}

数据请见【3】

3.结果：

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= 1.0(0.0,0.0)(1.0,1.0)(0.0,0.0)(0.0,0.0)(1.0,1.0)(1.0,1.0)(1.0,1.0)(0.0,0.0)(1.0,1.0)(1.0,1.0)

准确度100%

参考
【1】http://spark.apache.org/docs/1.5.2/mllib-guide.html
【2】http://spark.apache.org/docs/1.5.2/programming-guide.html
【3】https://github.com/xubo245/SparkLearning