use weka NaiveBayes Classification Prediction - Java Machine Learning AI

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Description

use weka NaiveBayes Classification Prediction

Demo Code

import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.supervised.attribute.AddClassification;

public class ClassificationPrediction {
    public static void main(String[] args) throws Exception {
        DataSource source = new DataSource("iris.arff");
        Instances traindata = source.getDataSet();
        traindata.setClassIndex(traindata.numAttributes() - 1);
        int numClasses = traindata.numClasses();
        for (int i = 0; i < numClasses; i++) {
            String classValue = traindata.classAttribute().value(i);
            System.out.println("the " + i + "th class value:" + classValue);
        }/*from  w  ww . java  2 s .  c o m*/
        /**
         * naive bayes classifier  
         */
        NaiveBayes nb = new NaiveBayes();
        nb.buildClassifier(traindata);
        /**
         * load test data
         */
        DataSource source2 = new DataSource("iris-unknown.arff");
        Instances testdata = source2.getDataSet();
        testdata.setClassIndex(testdata.numAttributes() - 1);

        /**
         * make prediction by naive bayes classifier
         */
        for (int j = 0; j < testdata.numInstances(); j++) {
            double actualClass = testdata.instance(j).classValue();
            String actual = testdata.classAttribute().value(
                    (int) actualClass);
            Instance newInst = testdata.instance(j);
            System.out.println("actual class:"
                    + newInst.stringValue(newInst.numAttributes() - 1));
            double preNB = nb.classifyInstance(newInst);
            String predString = testdata.classAttribute()
                    .value((int) preNB);
            System.out.println(actual + "," + predString);
        }

    }

}

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