org.apache.spark.mllib.tree.impurity.Gini Scala Examples

The following examples show how to use org.apache.spark.mllib.tree.impurity.Gini. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
Example 1
Source File: DecisionTreeTest.scala    From spark1.52   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.examples.mllib
import org.apache.spark.{ SparkConf, SparkContext }
import org.apache.spark.mllib.tree.DecisionTree
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.tree.configuration.Algo._
import org.apache.spark.mllib.tree.impurity.Gini

object DecisionTreeTest {
  def main(args: Array[String]) {
    val sparkConf = new SparkConf().setMaster("local[2]").setAppName("KMeansClustering")
    val sc = new SparkContext(sparkConf)
    val data = sc.textFile("../data/mllib/sample_tree_data.csv")    
    val parsedData = data.map { line =>
      val parts = line.split(',').map(_.toDouble)
      //LabeledPoint标记点是局部向量,向量可以是密集型或者稀疏型,每个向量会关联了一个标签(label)
      LabeledPoint(parts(0), Vectors.dense(parts.tail))
    }

    val maxDepth = 5//树的最大深度,为了防止过拟合,设定划分的终止条件
    val model = DecisionTree.train(parsedData, Classification, Gini, maxDepth)

    val labelAndPreds = parsedData.map { point =>
      val prediction = model.predict(point.features)
      (point.label, prediction)
    }
    val trainErr = labelAndPreds.filter(r => r._1 != r._2).count().toDouble / parsedData.count
    println("Training Error = " + trainErr)
  }
}