Weka Evaluation Classify #RandomForest #Android

WekaEvaluationAndroid

  InputStream is =getResources().openRawResource(R.raw.r4_reference); //arff
  BufferedReader datafile = new BufferedReader(new InputStreamReader(is));
  try {
    Instances m_Training = new Instances(datafile);
    m_Training.setClassIndex(m_Training.numAttributes() - 1);
    Filter m_Filter = ((Filter)Class.forName("weka.filters.unsupervised.instance.Randomize").newInstance());
    m_Filter.setInputFormat(m_Training);
    Instances localInstances = Filter.useFilter(m_Training, m_Filter);
    Classifier m_Classifier = Classifier.forName("weka.classifiers.trees.RandomForest", null);
    m_Classifier.buildClassifier(localInstances);
    Evaluation m_Evaluation = new Evaluation(localInstances);
  
    m_Evaluation.crossValidateModel(m_Classifier, localInstances, 10, m_Training.getRandomNumberGenerator(1L), new Object[0]);
    Log.e("Detail", m_Evaluation.toClassDetailsString());
    Log.e("Summary",m_Evaluation.toSummaryString());
    Log.e("Result","Correct:"+ m_Evaluation.correct()+":Wrong:"+m_Evaluation.incorrect()+":Correct(%):"+m_Evaluation.pctCorrect());

  }catch (Exception e){

  }