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copied!<p>In <a href="http://weka.wikispaces.com/Use+WEKA+in+your+Java+code#Classification-Classifying%20instances" rel="nofollow">http://weka.wikispaces.com/Use+WEKA+in+your+Java+code#Classification-Classifying%20instances</a> there is a quick how to for classifying a single instance.</p> <pre><code>//load model (saved from user interface) Classifier tree = (Classifier) weka.core.SerializationHelper.read("/some/where/j48.model"); // load unlabeled data Instances unlabeled = new Instances( new BufferedReader(new FileReader("/some/where/unlabeled.arff"))); // set class attribute unlabeled.setClassIndex(unlabeled.numAttributes() - 1); // create copy Instances labeled = new Instances(unlabeled); // label instances for (int i = 0; i < unlabeled.numInstances(); i++) { double clsLabel = tree.classifyInstance(unlabeled.instance(i)); labeled.instance(i).setClassValue(clsLabel); System.out.println(clsLabel + " -> " + unlabeled.classAttribute().value((int) clsLabel)); double[] dist =tree.distributionForInstance(unlabeled.instance(i)) for(int j=0; j<dist.length;j++){ System.print(unlabeled.classAttribute().value(j)+": " +dist[j]); } } </code></pre> <p><strong>Edit</strong> This method doesn't train, evaluate and save a model. This is something I usually do using the weka gui. ( <a href="http://weka.wikispaces.com/Serialization" rel="nofollow">http://weka.wikispaces.com/Serialization</a> ) This method uses a tree type model in the example with a nominal class, but that should be easily converted to a Naive Bayes example.</p>

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