List of errorRate() Examples

Examples of errorRate()

weka.classifiers.Evaluation.errorRate()
Returns the estimated error rate or the root mean squared error (if the class is numeric). If a cost matrix was given this error rate gives the average cost. @return the estimated error rate (between 0 and 1, or between 0 andmaximum cost)

Examples of weka.classifiers.Evaluation.errorRate()

      m_Classifiers[m_NumIterationsPerformed].buildClassifier(trainData);


      // Evaluate the classifier
      evaluation = new Evaluation(data);
      evaluation.evaluateModel(m_Classifiers[m_NumIterationsPerformed], training);
      epsilon = evaluation.errorRate();


      // Stop if error too small or error too big and ignore this model
      if (Utils.grOrEq(epsilon, 0.5) || Utils.eq(epsilon, 0)) {
  if (m_NumIterationsPerformed == 0) {
    m_NumIterationsPerformed = 1; // If we're the first we have to to use it

View Full Code Here

Examples of weka.classifiers.Evaluation.errorRate()

      /*      o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR", 
              trainCopy, 10, 
              null, new Random(m_randomSeed)); */
      o_Evaluation.crossValidateModel(oneR, trainCopy, m_folds, new Random(m_randomSeed));
    }
    errorRate = o_Evaluation.errorRate();
    return  (1 - errorRate)*100.0;
  }




  /**

View Full Code Here

Examples of weka.classifiers.Evaluation.errorRate()

  Instances test = trainData.testCV(m_NumFolds, j);
  m_Classifier.buildClassifier(train);
  evaluation.setPriors(train);
  evaluation.evaluateModel(m_Classifier, test);
      }
      double error = evaluation.errorRate();
      if (m_Debug) {
  System.err.println("Cross-validated error rate: " 
         + Utils.doubleToString(error, 6, 4));
      }
      if ((m_BestPerformance == -99) || (error < m_BestPerformance)) {

View Full Code Here

Examples of weka.classifiers.Evaluation.errorRate()


    // error of unpruned tree
    if (errors != null) {
      eval = new Evaluation(test);
      eval.evaluateModel(this, test);
      errors[0] = eval.errorRate();
    }


    int iteration = 0;
    double preAlpha = Double.MAX_VALUE;
    while (prune) {

View Full Code Here

Examples of weka.classifiers.Evaluation.errorRate()


      // log error
      if (errors != null) {
  eval = new Evaluation(test);
  eval.evaluateModel(this, test);
  errors[iteration] = eval.errorRate();
      }
      preAlpha = nodeToPrune.m_Alpha;


      //update errors/alphas
      treeErrors();

View Full Code Here

Examples of weka.classifiers.Evaluation.errorRate()


  //error of unpruned tree
  if (errors != null) {
      eval = new Evaluation(test);
      eval.evaluateModel(this, test);
      errors[0] = eval.errorRate(); 
  }  
       
  int iteration = 0;
  while (prune) {

View Full Code Here

Examples of weka.classifiers.Evaluation.errorRate()

       
      //log error
      if (errors != null) {
    eval = new Evaluation(test);
    eval.evaluateModel(this, test);
    errors[iteration] = eval.errorRate(); 
      }


      //update errors/alphas
      treeErrors();
      calculateAlphas();

View Full Code Here

Examples of weka.classifiers.Evaluation.errorRate()

     * @throws Exception if something goes wrong
     */
    protected double getErrorRate(Instances data) throws Exception {
  Evaluation eval = new Evaluation(data);
  eval.evaluateModel(this,data);
  return eval.errorRate();
    }


    /**
     * Returns the error of the probability estimates for the current model on a set of instances.
     * @param data the set of instances

View Full Code Here

Examples of weka.classifiers.Evaluation.errorRate()

    // calculate error rate if only root node
    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

View Full Code Here

Examples of weka.classifiers.Evaluation.errorRate()

        m_roots[i] = null; // cannot be expanded
        continue;
      }
      eval = new Evaluation(test[i]);
      eval.evaluateModel(m_roots[i], test[i]);
      if (m_UseErrorRate) expansionError += eval.errorRate();
      else expansionError += eval.rootMeanSquaredError();
      count ++;
    }
  }

View Full Code Here

0 1 2

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.