List of rootMeanSquaredError() Examples

Examples of rootMeanSquaredError()

weka.classifiers.Evaluation.rootMeanSquaredError()
Returns the root mean squared error. @return the root mean squared error

Examples of weka.classifiers.Evaluation.rootMeanSquaredError()

    result[current++] = new Double(eval.pctUnclassified());
    result[current++] = new Double(eval.totalCost());
    result[current++] = new Double(eval.avgCost());
    
    result[current++] = new Double(eval.meanAbsoluteError());
    result[current++] = new Double(eval.rootMeanSquaredError());
    result[current++] = new Double(eval.relativeAbsoluteError());
    result[current++] = new Double(eval.rootRelativeSquaredError());
    
    result[current++] = new Double(eval.SFPriorEntropy());
    result[current++] = new Double(eval.SFSchemeEntropy());

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()

    result[current++] = new Double(eval.pctIncorrect());
    result[current++] = new Double(eval.pctUnclassified());
    result[current++] = new Double(eval.kappa());
    
    result[current++] = new Double(eval.meanAbsoluteError());
    result[current++] = new Double(eval.rootMeanSquaredError());
    result[current++] = new Double(eval.relativeAbsoluteError());
    result[current++] = new Double(eval.rootRelativeSquaredError());
    
    result[current++] = new Double(eval.SFPriorEntropy());
    result[current++] = new Double(eval.SFSchemeEntropy());

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()

    int current = 0;
    result[current++] = new Double(train.numInstances());
    result[current++] = new Double(eval.numInstances());


    result[current++] = new Double(eval.meanAbsoluteError());
    result[current++] = new Double(eval.rootMeanSquaredError());
    result[current++] = new Double(eval.relativeAbsoluteError());
    result[current++] = new Double(eval.rootRelativeSquaredError());
    result[current++] = new Double(eval.correlationCoefficient());


    result[current++] = new Double(eval.SFPriorEntropy());

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()

    m_values = values;
    
    // Compute sum of squared errors
    Evaluation eval = new Evaluation(insts);
    eval.evaluateModel(this, insts);
    double msq = eval.rootMeanSquaredError();
    
    // Check whether this is the best attribute
    if (msq < m_minMsq) {
      m_minMsq = msq;
    } else {

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()

    int current = 0;
    result[current++] = new Double(train.numInstances());
    result[current++] = new Double(eval.numInstances());


    result[current++] = new Double(eval.meanAbsoluteError());
    result[current++] = new Double(eval.rootMeanSquaredError());
    result[current++] = new Double(eval.relativeAbsoluteError());
    result[current++] = new Double(eval.rootRelativeSquaredError());
    result[current++] = new Double(eval.correlationCoefficient());


    result[current++] = new Double(eval.SFPriorEntropy());

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()

    if (expansion==0) {
      m_roots[i].m_isLeaf = true;
      eval = new Evaluation(test[i]);
      eval.evaluateModel(m_roots[i], test[i]);
      if (m_UseErrorRate) expansionError += eval.errorRate();
      else expansionError += eval.rootMeanSquaredError();
      count ++;
    }


    // make tree - expand one node at a time
    else {

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()

        continue;
      }
      eval = new Evaluation(test[i]);
      eval.evaluateModel(m_roots[i], test[i]);
      if (m_UseErrorRate) expansionError += eval.errorRate();
      else expansionError += eval.rootMeanSquaredError();
      count ++;
    }
  }


  // no tree can be expanded any more

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()

  m_roots[i].m_isLeaf = true;
  Evaluation eval = new Evaluation(test[i]);
  eval.evaluateModel(m_roots[i], test[i]);
  double error;
  if (m_UseErrorRate) error = eval.errorRate();
  else error = eval.rootMeanSquaredError();
  modelError[i].addElement(new Double(error));


  m_roots[i].m_isLeaf = false;
  BFTree nodeToSplit = (BFTree)
  (((FastVector)(parallelBFElements[i].elementAt(0))).elementAt(0));

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()


  Evaluation eval = new Evaluation(test);
  eval.evaluateModel(root, test);
  double error;
  if (useErrorRate) error = eval.errorRate();
  else error = eval.rootMeanSquaredError();
  modelError.addElement(new Double(error));
      }


      if (BestFirstElements.size()!=0) {
  FastVector nextSplitElement = (FastVector)BestFirstElements.elementAt(0);

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()

    result[current++] = new Double(eval.pctIncorrect());
    result[current++] = new Double(eval.pctUnclassified());
    result[current++] = new Double(eval.kappa());
    
    result[current++] = new Double(eval.meanAbsoluteError());
    result[current++] = new Double(eval.rootMeanSquaredError());
    result[current++] = new Double(eval.relativeAbsoluteError());
    result[current++] = new Double(eval.rootRelativeSquaredError());
    
    result[current++] = new Double(eval.SFPriorEntropy());
    result[current++] = new Double(eval.SFSchemeEntropy());

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