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.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()

    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 (printModelPerformances) {
  String output = new String(m_chosen_models[i]
                                             .getStringRepresentation()
                                             + ": ");
  output += "\tRMSE:" + evalModel.rootMeanSquaredError();
  output += "\tACC:" + evalModel.pctCorrect();
  if (test.numClasses() == 2) {
    // For multiclass problems, we could print these too, but
    // it's
    // not clear which class we should use in that case... so

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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()

    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()

      }
      buildLinearModel(m_indices);
    }
    nodeModelEval = new Evaluation(m_instances);
    nodeModelEval.evaluateModel(m_nodeModel, m_instances);
    m_rootMeanSquaredError = nodeModelEval.rootMeanSquaredError();
    // save space
    if (!m_saveInstances) {
      m_instances = new Instances(m_instances, 0);
    }
  }

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()


      // count the constant term as a paramter for a leaf
      // Evaluate the model
      nodeModelEval.evaluateModel(m_nodeModel, m_instances);


      m_rootMeanSquaredError = nodeModelEval.rootMeanSquaredError();
    } else {


      // Prune the left and right subtrees
      if (m_left != null) {
  m_left.prune();

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()

      double rmsModel;
      double adjustedErrorModel;


      nodeModelEval.evaluateModel(m_nodeModel, m_instances);


      rmsModel = nodeModelEval.rootMeanSquaredError();
      adjustedErrorModel = rmsModel 
  * pruningFactor(m_numInstances, 
      m_nodeModel.numParameters() + 1);


      // Evaluate this node (ie its left and right subtrees)

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Examples of weka.classifiers.Evaluation.rootMeanSquaredError()

      double     adjustedErrorNode;
      int   l_params = 0, r_params = 0;


      nodeEval.evaluateModel(this, m_instances);


      rmsSubTree = nodeEval.rootMeanSquaredError();


      if (m_left != null) {
  l_params = m_left.numParameters();
      }

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