weka Random Forest Classifier - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

weka Random Forest Classifier

Demo Code

import java.io.BufferedReader;
import java.io.FileReader;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.evaluation.NominalPrediction;
import weka.classifiers.trees.RandomForest;
import weka.core.FastVector;
import weka.core.Instances;
import java.lang.Object;
import weka.classifiers.evaluation.output.prediction.PlainText;

public class RandomForestClassifier {
    public static void main(String[] args) throws Exception {
        BufferedReader readerTrain = new BufferedReader(new FileReader(
                args[0]));//from w  w w .jav  a 2 s .c o  m
        BufferedReader readerTest = new BufferedReader(new FileReader(
                args[1]));
        Instances train = new Instances(readerTrain);
        Instances test = new Instances(readerTest);
        train.setClassIndex(0);
        test.setClassIndex(0);
        StringBuffer predsBuffer = new StringBuffer();
        PlainText plainText = new PlainText();
        plainText.setHeader(train);
        plainText.setBuffer(predsBuffer);
        int treeNum = Integer.parseInt(args[2]);
        RandomForest cls = new RandomForest();
        cls.setNumTrees(treeNum);
        cls.buildClassifier(train);
        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(cls, test, plainText);
        System.out.println(eval
                .toSummaryString("\nResults\n======\n", true));
        System.out.println(eval.toClassDetailsString());
        System.out.println(predsBuffer.toString());
        readerTrain.close();
        readerTest.close();
    }
}

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