Model evaluation

We will now look at how to evaluate the classifier that we have trained. Let's start with the code.

We'll start by importing the following classes:

import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.classifiers.trees.J48;
import weka.classifiers.Evaluation;
import java.util.Random;

This time, we'll use the Evaluation class from the weka.classifiers package, and a Random class for some random value generation.

 

The DataSource that we'll be using is the segment-challenge.arff file. We are using this because it has a test dataset, and it is also one of the datasets that comes with Weka. We'll assign it to our Instances object, and we will then tell Weka which attribute is the class attribute. We'll set the flags for our decision tree classifier and create an object for our decision tree classifier. Then, we'll set the options, and we'll build the classifier. We performed the same in the previous section:

public static void main(String...