这期内容当中小编将会给大家带来有关使用Spark怎么实现一个随机森林，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。
具体内容如下
计算
安全
数据库
网络和加速
企业服务
public class RandomForestClassficationTest extends TestCase implements Serializable
{
/**
*
*/
private static final long serialVersionUID = 7802523720751354318L;
class PredictResult implements Serializable{
/**
*
*/
private static final long serialVersionUID = -168308887976477219L;
double label;
double prediction;
public PredictResult(double label,double prediction){
this.label = label;
this.prediction = prediction;
}
@Override
public String toString(){
return this.label + " : " + this.prediction ;
}
}
public void test_randomForest() throws JAXBException{
SparkConf sparkConf = new SparkConf();
sparkConf.setAppName("RandomForest");
sparkConf.setMaster("local");
SparkContext sc = new SparkContext(sparkConf);
String dataPath = RandomForestClassficationTest.class.getResource("/").getPath() + "/sample_libsvm_data.txt";
RDD dataSet = MLUtils.loadLibSVMFile(sc, dataPath);
RDD[] rddList = dataSet.randomSplit(new double[]{0.7,0.3},1);
RDD trainingData = rddList[0];
RDD testData = rddList[1];
ClassTag labelPointClassTag = trainingData.elementClassTag();
JavaRDD trainingJavaData = new JavaRDD(trainingData,labelPointClassTag);
int numClasses = 2;
Map categoricalFeatureInfos = new HashMap();
int numTrees = 3;
String featureSubsetStrategy = "auto";
String impurity = "gini";
int maxDepth = 4;
int maxBins = 32;
/**
* 1 numClasses分类个数为2
* 2 numTrees 表示的是随机森林中树的个数
* 3 featureSubsetStrategy
* 4
*/
final RandomForestModel model = RandomForest.trainClassifier(trainingJavaData,
numClasses,
categoricalFeatureInfos,
numTrees,
featureSubsetStrategy,
impurity,
maxDepth,
maxBins,
1);
JavaRDD testJavaData = new JavaRDD(testData,testData.elementClassTag());
JavaRDD predictRddResult = testJavaData.map(new Function(){
/**
*
*/
private static final long serialVersionUID = 1L;
public PredictResult call(LabeledPoint point) throws Exception {
// TODO Auto-generated method stub
double pointLabel = point.label();
double prediction = model.predict(point.features());
PredictResult result = new PredictResult(pointLabel,prediction);
return result;
}
});
List predictResultList = predictRddResult.collect();
for(PredictResult result:predictResultList){
System.out.println(result.toString());
}
System.out.println(model.toDebugString());
}
}得到的随机森林的展示结果如下：
TreeEnsembleModel classifier with 3 trees
Tree 0:
If (feature 435 <= 0.0)
If (feature 516 <= 0.0)
Predict: 0.0
Else (feature 516 > 0.0)
Predict: 1.0
Else (feature 435 > 0.0)
Predict: 1.0
Tree 1:
If (feature 512 <= 0.0)
Predict: 1.0
Else (feature 512 > 0.0)
Predict: 0.0
Tree 2:
If (feature 377 <= 1.0)
Predict: 0.0
Else (feature 377 > 1.0)
If (feature 455 <= 0.0)
Predict: 1.0
Else (feature 455 > 0.0)
Predict: 0.0上述就是小编为大家分享的使用Spark怎么实现一个随机森林了，如果刚好有类似的疑惑，不妨参照上述分析进行理解。如果想知道更多相关知识，欢迎关注亿速云行业资讯频道。
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