List of areaUnderROC() Examples

Examples of areaUnderROC()

weka.classifiers.Evaluation.areaUnderROC()
Returns the area under ROC for those predictions that have been collected in the evaluateClassifier(Classifier, Instances) method. Returns Utils.missingValue() if the area is not available. @param classIndex the index of the class to consider as "positive" @return the area under the ROC curve or not a number

Examples of weka.classifiers.Evaluation.areaUnderROC()

    result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
    result[current++] = new Double(eval.precision(m_IRclass));
    result[current++] = new Double(eval.recall(m_IRclass));
    result[current++] = new Double(eval.fMeasure(m_IRclass));
    result[current++] = new Double(eval.areaUnderROC(m_IRclass));
    
    // Weighted IR stats
    result[current++] = new Double(eval.weightedTruePositiveRate());
    result[current++] = new Double(eval.weightedFalsePositiveRate());
    result[current++] = new Double(eval.weightedTrueNegativeRate());

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Examples of weka.classifiers.Evaluation.areaUnderROC()

          long elapsedTime = System.currentTimeMillis() - millis;


          double aucSum = 0.0;
          double sumClassProps = 0;
          for (int c = 0; c < data.numClasses(); c++) {
            if (Double.isNaN(eval.areaUnderROC(c)))
              continue;
            aucSum += eval.areaUnderROC(c) * classProps[c];
            // this should sum to 1.0 in the end, as all the classes with AUC==NaN should have weight 0
            sumClassProps += classProps[c];
          }

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Examples of weka.classifiers.Evaluation.areaUnderROC()

          double aucSum = 0.0;
          double sumClassProps = 0;
          for (int c = 0; c < data.numClasses(); c++) {
            if (Double.isNaN(eval.areaUnderROC(c)))
              continue;
            aucSum += eval.areaUnderROC(c) * classProps[c];
            // this should sum to 1.0 in the end, as all the classes with AUC==NaN should have weight 0
            sumClassProps += classProps[c];
          }

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Examples of weka.classifiers.Evaluation.areaUnderROC()

    result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
    result[current++] = new Double(eval.precision(m_IRclass));
    result[current++] = new Double(eval.recall(m_IRclass));
    result[current++] = new Double(eval.fMeasure(m_IRclass));
    result[current++] = new Double(eval.areaUnderROC(m_IRclass));
    
    // Weighted IR stats
    result[current++] = new Double(eval.weightedTruePositiveRate());
    result[current++] = new Double(eval.weightedFalsePositiveRate());
    result[current++] = new Double(eval.weightedTrueNegativeRate());

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Examples of weka.classifiers.Evaluation.areaUnderROC()

    // it's
    // not clear which class we should use in that case... so
    // instead
    // we only print these metrics for binary classification
    // problems.
    output += "\tROC:" + evalModel.areaUnderROC(1);
    output += "\tPREC:" + evalModel.precision(1);
    output += "\tFSCR:" + evalModel.fMeasure(1);
  }
  System.out.println(output);
      }

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Examples of weka.classifiers.Evaluation.areaUnderROC()

    result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
    result[current++] = new Double(eval.precision(m_IRclass));
    result[current++] = new Double(eval.recall(m_IRclass));
    result[current++] = new Double(eval.fMeasure(m_IRclass));
    result[current++] = new Double(eval.areaUnderROC(m_IRclass));
    
    // Timing stats
    result[current++] = new Double(trainTimeElapsed / 1000.0);
    result[current++] = new Double(testTimeElapsed / 1000.0);
    if(canMeasureCPUTime) {

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Examples of weka.classifiers.Evaluation.areaUnderROC()

    result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
    result[current++] = new Double(eval.precision(m_IRclass));
    result[current++] = new Double(eval.recall(m_IRclass));
    result[current++] = new Double(eval.fMeasure(m_IRclass));
    result[current++] = new Double(eval.areaUnderROC(m_IRclass));
    
    // Weighted IR stats
    result[current++] = new Double(eval.weightedTruePositiveRate());
    result[current++] = new Double(eval.weightedFalsePositiveRate());
    result[current++] = new Double(eval.weightedTrueNegativeRate());

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