Spark中组件Mllib的学习33之决策树（使用Gini）.md

更多代码请见：https://github.com/xubo245/SparkLearning
Spark中组件Mllib的学习之分类篇

1解释
决策树：Decision Trees
请见【4】【5】
数据每次是随机划分，所以准确率每次不一定

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.classification

import java.text.SimpleDateFormat
import java.util.Date

import org.apache.spark.mllib.tree.DecisionTree
import org.apache.spark.mllib.tree.model.DecisionTreeModel
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.{SparkConf, SparkContext}

/**
  * Created by xubo on 2016/5/23.
  */
object DecisionTreesLearning {
  def main(args: Array[String]) {
    val conf = new SparkConf().setMaster("local[4]").setAppName(this.getClass().getSimpleName().filter(!_.equals('$')))
    val sc = new SparkContext(conf)

    // Load and parse the data file.
    val data = MLUtils.loadLibSVMFile(sc, "file/data/mllib/input/classification/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 DecisionTree model.
    //  Empty categoricalFeaturesInfo indicates all features are continuous.
    val numClasses = 2
    val categoricalFeaturesInfo = Map[Int, Int]()
    val impurity = "gini"
    val maxDepth = 5
    val maxBins = 32

    val model = DecisionTree.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo,
      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 tree model:\n" + model.toDebugString)
    println("data.count:" + data.count())
    println("trainingData.count:" + trainingData.count())
    println("testData.count:" + testData.count())
    println("model.depth:"+model.depth)
    println("model.numNodes:"+model.numNodes)
    println("model.topNode:"+model.topNode)

    println("labelAndPreds")
    labelAndPreds.take(30).foreach(println)
    // Save and load model
    //    val iString = new SimpleDateFormat("yyyyMMddHHmmssSSS").format(new Date())
    //    val path = "file/data/mllib/output/classification/DecisionTreesLearning" + iString + "/result"
    //    model.save(sc, path)
    //    val sameModel = DecisionTreeModel.load(sc, path)
    //    println(sameModel.algo)
    sc.stop
  }
}

```

3.结果：

```
Test Error = 0.0
Learned classification tree model:
DecisionTreeModel classifier of depth 2 with 5 nodes
  If (feature 434 <= 0.0)
   If (feature 100 <= 165.0)
    Predict: 0.0
   Else (feature 100 > 165.0)
    Predict: 1.0
  Else (feature 434 > 0.0)
   Predict: 1.0

data.count:100
trainingData.count:78
testData.count:22
model.depth:2
model.numNodes:5
model.topNode:id = 1, isLeaf = false, predict = 1.0 (prob = 0.5384615384615384), impurity = 0.49704142011834324, split = Some(Feature = 434, threshold = 0.0, featureType = Continuous, categories = List()), stats = Some(gain = 0.47209339517031834, impurity = 0.49704142011834324, left impurity = 0.05259313367421467, right impurity = 0.0)
labelAndPreds
(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)
(0.0,0.0)
(1.0,1.0)
(0.0,0.0)
(1.0,1.0)
(1.0,1.0)
(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)
(1.0,1.0)
(1.0,1.0)
(1.0,1.0)
```

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
【4】http://blog.csdn.net/dark_scope/article/details/13168827
【5】http://spark.apache.org/docs/1.5.2/mllib-decision-tree.html