更多代碼請(qǐng)見(jiàn)：https://github.com/xubo245/SparkLearning?
Spark中組件Mllib的學(xué)習(xí)之分類(lèi)篇?
1解釋?
GBRT（Gradient Boost Regression Tree）漸進(jìn)梯度回歸樹(shù)?
同樣的setCategoricalFeaturesInfo有問(wèn)題。注釋掉了。

2.代碼：

/**
? * @author xubo
? * ? ? ? ? ref:Spark MlLib機(jī)器學(xué)習(xí)實(shí)戰(zhàn)
? * ? ? ? ? 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.util.MLUtils
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.tree.GradientBoostedTrees
import org.apache.spark.mllib.tree.configuration.BoostingStrategy
import org.apache.spark.mllib.tree.model.{DecisionTreeModel, GradientBoostedTreesModel}
import org.apache.spark.mllib.util.MLUtils
import java.util.Map
import org.apache.spark.mllib.tree.GradientBoostedTrees
import org.apache.spark.mllib.tree.configuration.BoostingStrategy
import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
import org.apache.spark.mllib.util.MLUtils

/**
? * Created by xubo on 2016/5/23.
? */
object GBTs2Regression {
? 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 GradientBoostedTrees model.
? ? // ?The defaultParams for Classification use LogLoss by default.
? ? val boostingStrategy = BoostingStrategy.defaultParams("Regression")
? ? boostingStrategy.setNumIterations(3)
? ? boostingStrategy.treeStrategy.setMaxDepth(5)
? ? // ? ?boostingStrategy.treeStrategy.setCategoricalFeaturesInfo(Map[Int, Int]())

? ? // Train a GradientBoostedTrees model.
? ? // ?The defaultParams for Regression use SquaredError by default.
? ? // ? ?val boostingStrategy = BoostingStrategy.defaultParams("Regression")
? ? // ? ?boostingStrategy.numIterations = 3 // Note: Use more iterations in practice.
? ? // ? ?boostingStrategy.treeStrategy.maxDepth = 5
? ? // ? ?// ?Empty categoricalFeaturesInfo indicates all features are continuous.
? ? // ? ?boostingStrategy.treeStrategy.categoricalFeaturesInfo = Map[Int, Int]()

? ? val model = GradientBoostedTrees.train(trainingData, boostingStrategy)

? ? // Evaluate model on test instances and compute test error
? ? val labelsAndPredictions = testData.map { point =>
? ? ? val prediction = model.predict(point.features)
? ? ? (point.label, prediction)
? ? }
? ? val testMSE = labelsAndPredictions.map { case (v, p) => math.pow((v - p), 2) }.mean()
? ? println("Test Mean Squared Error = " + testMSE)
? ? println("Learned regression GBT model:\n" + model.toDebugString)

? ? println("data.count:" + data.count())
? ? println("trainingData.count:" + trainingData.count())
? ? println("testData.count:" + testData.count())
? ? println("model.algo:" + model.algo)
? ? println("model.trees:" + model.trees)
? ? println("model.treeWeights:" + model.treeWeights)

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

3.結(jié)果：

Test Mean Squared Error = 0.06896551724137932
Learned regression GBT model:
TreeEnsembleModel regressor with 3 trees

? Tree 0:
? ? If (feature 406 <= 72.0)
? ? ?If (feature 99 <= 0.0)
? ? ? Predict: 0.0
? ? ?Else (feature 99 > 0.0)
? ? ? Predict: 1.0
? ? Else (feature 406 > 72.0)
? ? ?Predict: 1.0
? Tree 1:
? ? Predict: 0.0
? Tree 2:
? ? Predict: 0.0

data.count:100
trainingData.count:71
testData.count:29
model.algo:Regression
model.trees:[Lorg.apache.spark.mllib.tree.model.DecisionTreeModel;@5e9a7c29
model.treeWeights:[D@78bf694d

參考?
【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
?

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