weka Cross Validate via classifiers.Evaluation - Java Machine Learning AI

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Description

weka Cross Validate via classifiers.Evaluation

Demo Code

import java.util.Random;

import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;

public class CrossValidate {
    public static void main(String[] args) throws Exception {
        DataSource source = new DataSource("iris.arff");
        Instances dataset = source.getDataSet();
        dataset.setClassIndex(dataset.numAttributes() - 1);

        NaiveBayes nb = new NaiveBayes();
        int fold = 10;
        int seed = 1;
        Random rand = new Random(seed);
        Instances randData = new Instances(dataset);
        randData.randomize(rand);// w w w .ja va2s . co  m
        if (randData.classAttribute().isNominal())
            randData.stratify(fold);
        double averagecorrect = 0;
        for (int n = 0; n < fold; n++) {
            Evaluation eval = new Evaluation(randData);
            Instances train = randData.trainCV(fold, n);
            Instances test = randData.testCV(fold, n);
            nb.buildClassifier(train);
            eval.evaluateModel(nb, test);
            double correct = eval.pctCorrect();
            averagecorrect = averagecorrect + correct;
            System.out.println("the " + n + "th cross validation:"
                    + eval.toSummaryString());

        }
        System.out.println("the average correction rate of " + fold
                + " cross validation: " + averagecorrect / fold);
    }
}

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