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.jarlibrary - The
collective-classification-<date>.jarlibrary
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{...
