Weka java code for Random Forest Cross Validation

Random Forest Classifier with Cross Validation testing. Note that you have to add Weka.jar to the java path.
java code

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package wekatest;

import java.io.BufferedReader;
import java.io.FileReader;
import java.util.Random;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.trees.RandomForest;
 
import weka.core.Instances;

/**
 *
 * @author samy
 */
public class WekaTest {

    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) throws Exception {
        BufferedReader br = null;
        int numFolds = 10;
        br = new BufferedReader(new FileReader("D:\\martin\\DataSet\\Mozilla.arff"));

        Instances trainData = new Instances(br);
        trainData.setClassIndex(trainData.numAttributes() - 1);
        br.close();
        RandomForest rf = new RandomForest();
        rf.setNumTrees(100);
        
     //   rf.buildClassifier(trainData);
        Evaluation evaluation = new Evaluation(trainData);
        evaluation.crossValidateModel(rf, trainData, numFolds, new Random(1));
         
        
        System.out.println(evaluation.toSummaryString("\nResults\n======\n", true));
        System.out.println(evaluation.toClassDetailsString());
        System.out.println("Results For Class -1- ");
        System.out.println("Precision=  " + evaluation.precision(0));
        System.out.println("Recall=  " + evaluation.recall(0));
        System.out.println("F-measure=  " + evaluation.fMeasure(0));
        System.out.println("Results For Class -2- ");
        System.out.println("Precision=  " + evaluation.precision(1));
        System.out.println("Recall=  " + evaluation.recall(1));
        System.out.println("F-measure=  " + evaluation.fMeasure(1));



    }
}