如何識別在weka中錯誤分類的確切實例

Question

這裏是我的代碼，我使用的是weka API。我想打印出錯誤分類的實例和準確分類的實例。請幫助我，或者告訴我有關能夠做我想做的任何其他文本分類的Java API。

public void evaluation() throws Exception{ 
    BufferedReader reader=null; 
    reader= new BufferedReader(new FileReader("SparseDTM.arff")); 

    Instances train= new Instances(reader); 
    train.setClassIndex(0); 
    train.toSummaryString(); 
    reader.close(); 
    SMO svm=new SMO(); 
    svm.buildClassifier(train); 

    NaiveBayes nB = new NaiveBayes(); 
    nB.buildClassifier(train); 

    weka.classifiers.Evaluation eval= new weka.classifiers.Evaluation(train); 
    eval.crossValidateModel(nB, train,10,new Random(1)); 
    //eval.crossValidateModel(nB, train,10,new Random(1), new Object[] { }); 

    System.out.println("\n\t************Results by Naive Bayes Classifier************\n"); 
    System.out.println(eval.toSummaryString("", true)); 
    System.out.println(eval.toClassDetailsString()); 
// System.out.println("F Measure: "+eval.fMeasure(1) + " " + "Precision: "+eval.precision(1) + " " + "Precision: "+eval.recall(1)); 
// System.out.println("Correct :" + eval.correct()); 
// System.out.println("Weighted True Negative Rate: " + eval.weightedTrueNegativeRate()); 
// System.out.println("Weighted False Positive Rate:" + eval.weightedFalsePositiveRate()); 
// System.out.println("Weighted False Negative Rate:" + eval.weightedFalseNegativeRate()); 
// System.out.println("Weighted True Positive Rate:" + eval.weightedTruePositiveRate()); 
    System.out.println(eval.toMatrixString()); 
    } 

Answer 1

下面是一個方法，可以幫助你解決你的問題。所以，你可以編輯它來達到你的目標。

public void showPredictions(){  

    BufferedReader reader=null; 
    reader= new BufferedReader(new FileReader("SparseDTM.arff")); 

    Instances data = new Instances(reader); 

    double[] predictions; 
    try { 

     NaiveBayes classifier = new NaiveBayes(); 
     classifier.buildClassifier(data); 

     predictions = eval.evaluateModel(classifier, data); 

     int classIndex = data.numAttributes()-1; 
     // getting the array of predictions for each instance 
     System.out.println("predictions: "); 
     for (int i=0; i < data.numInstances(); i++) { 
      double realValue = testData.instance(i).classValue(); // test or train data. 
      System.out.print("Real Value: " + testData.instance(i).stringValue(classIndex)); 
      System.out.println("\tClassification predicted value: " + predictions[i]); 

      if(realValue != predictions[i]) { 
       System.out.println("misclassified instance: " + testData.instance(i).toString()); 
      } 
     }  
    } catch (Exception e) { 
     e.printStackTrace(); 
    } 
} 

通過「數據」替換「TESTDATA」如果你能遵守相關題庫組誤分類情況。否則，你必須提供一個測試集。