Question

我有以下代码，其中我在训练数据集上应用了分类器，我想将其保存（使用交叉验证）作为模型。并将其再次应用于测试数据集。

import weka.core.Instances;
import weka.classifiers.trees.J48;
import weka.classifiers.Evaluation;
import java.util.Random;
import java.io.BufferedReader;
import java.io.FileReader ;

public class EECS_738
{
   public static void main(String[] args) throws Exception {
   // training
      BufferedReader reader = null;
      reader=new BufferedReader(new FileReader("/Users/Sumiah/Desktop/ProjectTraining&TestingData/project/EECS738_Train_Project.arff"));
      Instances train =new Instances (reader);
      train.setClassIndex(0);     
      reader.close();

      J48 j48 = new J48();
      j48.buildClassifier(train);
      Evaluation eval = new Evaluation(train);
      eval.crossValidateModel(j48, train, 10 , new Random(1));

      System.out.println(eval.toSummaryString("\n Results \n=====\n",true));
      System.out.println(eval.fMeasure(1)+" "+eval.precision(1)+" "+eval.recall(1)+" ");           
   }   
}

Answer 1

对于大于3.5.5的版本，它很简单：

J48 j48 = new J48();
j48.buildClassifier(train);
weka.core.SerializationHelper.write("/some/where/j48.model", j48);

加载只是：

Classifier cls = (Classifier) weka.core.SerializationHelper.read("/some/where/j48.model");

有关详细信息以及如何在旧版本中执行此操作，请参阅here。

如何保存模型并将其应用于java上的测试数据集

1 个答案: