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()

  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)) {

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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

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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 ++;
    }
  }

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Examples of weka.classifiers.Evaluation.errorRate()


  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)

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Examples of weka.classifiers.Evaluation.errorRate()

  }


  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) {

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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;
  }




  /**

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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) {

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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();

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Examples of weka.classifiers.Evaluation.errorRate()

  currentClassifier.buildClassifier(train);
  evaluation = new Evaluation(train);
  evaluation.evaluateModel(currentClassifier, test);
      }


      double error = evaluation.errorRate();
      if (m_Debug) {
  System.err.println("Error rate: " + Utils.doubleToString(error, 6, 4)
         + " for classifier "
         + currentClassifier.getClass().getName());
      }

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Examples of weka.classifiers.Evaluation.errorRate()

  // Build and evaluate classifier
  m_Classifiers[m_NumIterationsPerformed].buildClassifier(sample);
  evaluation = new Evaluation(data);
  evaluation.evaluateModel(m_Classifiers[m_NumIterationsPerformed], 
         training);
  epsilon = evaluation.errorRate();
  resamplingIterations++;
      } while (Utils.eq(epsilon, 0) && 
        (resamplingIterations < MAX_NUM_RESAMPLING_ITERATIONS));
        
      // Stop if error too big or 0

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