use weka J48 tree classifiers - Java Machine Learning AI

Java examples for Machine Learning AI:weka

  1. HOME
  2. Java
  3. Machine Learning AI
  4. weka

Description

use weka J48 tree classifiers

Demo Code

/*/*  w w  w.j  a  v a2  s. co m*/
 *  How to use WEKA API in Java 
 *  Copyright (C) 2014 
 *  @author Dr Noureddin M. Sadawi (noureddin.sadawi@gmail.com)
 *  
 *  This program is free software: you can redistribute it and/or modify
 *  it as you wish ... 
 *  I ask you only, as a professional courtesy, to cite my name, web page 
 *  and my YouTube Channel!
 *  
 */

package weka.api;

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

public class Evaluate {
    public static void main(String args[]) throws Exception {
        //load datasets
        DataSource source = new DataSource(
                "/home/likewise-open/ACADEMIC/csstnns/Desktop/iris.arff");
        Instances dataset = source.getDataSet();
        //set class index to the last attribute
        dataset.setClassIndex(dataset.numAttributes() - 1);
        //create and build the classifier!
        J48 tree = new J48();
        tree.buildClassifier(dataset);

        Evaluation eval = new Evaluation(dataset);
        Random rand = new Random(1);
        int folds = 10;

        //Notice we build the classifier with the training dataset
        //we initialize evaluation with the training dataset and then
        //evaluate using the test dataset

        //test dataset for evaluation
        DataSource source1 = new DataSource(
                "/home/likewise-open/ACADEMIC/csstnns/Desktop/iris-test.arff");
        Instances testDataset = source1.getDataSet();
        //set class index to the last attribute
        testDataset.setClassIndex(testDataset.numAttributes() - 1);
        //now evaluate model
        //eval.evaluateModel(tree, testDataset);
        eval.crossValidateModel(tree, testDataset, folds, rand);
        System.out.println(eval.toSummaryString("Evaluation results:\n",
                false));

        System.out.println("Correct % = " + eval.pctCorrect());
        System.out.println("Incorrect % = " + eval.pctIncorrect());
        System.out.println("AUC = " + eval.areaUnderROC(1));
        System.out.println("kappa = " + eval.kappa());
        System.out.println("MAE = " + eval.meanAbsoluteError());
        System.out.println("RMSE = " + eval.rootMeanSquaredError());
        System.out.println("RAE = " + eval.relativeAbsoluteError());
        System.out.println("RRSE = " + eval.rootRelativeSquaredError());
        System.out.println("Precision = " + eval.precision(1));
        System.out.println("Recall = " + eval.recall(1));
        System.out.println("fMeasure = " + eval.fMeasure(1));
        System.out.println("Error Rate = " + eval.errorRate());
        //the confusion matrix
        System.out.println(eval
                .toMatrixString("=== Overall Confusion Matrix ===\n"));

    }
}

Related Tutorials