Making predictions with semi-supervised machine learning models

Now, we'll look into how to make predictions using our trained model. Consider the following code:

import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.classifiers.collective.functions.LLGC;
import weka.classifiers.collective.evaluation.Evaluation;

We will be importing two JAR libraries, as follows:

• The weka.jar library
• The collective-classification-<date>.jar library

Therefore, we will take the two base classes, Instances and DataSource, and we will use the LLGC class (since we have trained our model using LLGC) from the collective-classifications package, as well as the Evaluation class from the collective-classifications package.

We will first assign an ARFF file to our DataSource object; we'll read it into the memory, in an Instances object. We'll assign a class attribute to our Instances object, and then, we will build our model:

public static void main(String[] args) {
    try{...