Now, we'll look at how to predict a class using our test dataset. Let's start with the code. We'll use the following packages:

import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.classifiers.trees.J48;
import weka.core.Instance;

Notice that this time, we'll be using a new class: an Instance class from the weka.core package. This will help us to predict the class, using our test dataset. Then, as usual, we'll be reading our dataset into the src object, and we'll assign it to a dt object. We'll tell Weka which class attribute will be setting the attributes for our decision tree classifier in this dataset. Then, we'll create a decision tree classifier, set the objects for our decision tree classifier, and build the classifier, as follows:

public static void main(String[] args) { 
    // TODO code application logic here
    try {
        DataSource src = new DataSource("/Users/admin/Documents/NetBeansProjects/MakingPredictions/segment...