Java 在j48树weka中设置交叉验证数

Java 在j48树weka中设置交叉验证数,java,weka,Java,Weka,我想使用weka j48树进行5交叉验证。这是我到目前为止的代码 public class WekaJvMain { public static void main(String[] args) { try { CSV2Arff converter =new CSV2Arff(); converter.convert(); DataSource source = new

我想使用weka j48树进行5交叉验证。这是我到目前为止的代码

public class WekaJvMain {
    public static void main(String[] args) {
         try
         {  
             CSV2Arff converter =new CSV2Arff();
             converter.convert();

             DataSource source = new DataSource("data.arff");
             Instances train = source.getDataSet();

             train.setClassIndex(train.numAttributes() - 1);  // setting class attribute

             // classifier
             J48 j48 = new J48();
             j48.setUnpruned(true);        // using an unpruned J48

             j48.buildClassifier(train);
             System.out.print(j48.graph());

         }
         catch(Exception e)
         {
             e.printStackTrace();
         }      
    }
}

该代码对数据进行训练并打印出j48树。但是,我找不到如何设置交叉验证的折叠数?请详细解释,我不擅长Java。

这是您的代码,并对j48分类器进行了5倍交叉验证评估。在训练最终分类器之前进行评估非常重要。可以找到其他信息


我理解正确吗?你想用5倍交叉验证来评估J48?是的,用5倍或更多。
public class WekaJvMain {
    public static void main(String[] args) {
         try
         {  
             CSV2Arff converter =new CSV2Arff();
             converter.convert();

             DataSource source = new DataSource("data.arff");
             Instances train = source.getDataSet();

             train.setClassIndex(train.numAttributes() - 1);  // setting class attribute

             // classifier
             J48 j48 = new J48();
             j48.setUnpruned(true);        // using an unpruned J48

             //evaluate j48 with cross validation
             Evaluation eval=new Evaluation(train);

             //first supply the classifier
             //then the training data
             //number of folds
             //random seed
             eval.crossValidateModel(j48, train, 5, new Random(1));
             System.out.println("Percent correct: "+
                                Double.toString(eval.pctCorrect()));


             j48.buildClassifier(train);
             System.out.print(j48.graph());

         }
         catch(Exception e)
         {
             e.printStackTrace();
         }      
    }
}