List of evaluateModel() Examples

Examples of evaluateModel()

weka.classifiers.Evaluation.evaluateModel()
Evaluates a classifier with the options given in an array of strings.
Valid options are:
-t filename
Name of the file with the training data. (required)
-T filename
Name of the file with the test data. If missing a cross-validation is performed.
-c index
Index of the class attribute (1, 2, ...; default: last).
-x number
The number of folds for the cross-validation (default: 10).
-no-cv
No cross validation. If no test file is provided, no evaluation is done.
-split-percentage percentage
Sets the percentage for the train/test set split, e.g., 66.
-preserve-order
Preserves the order in the percentage split instead of randomizing the data first with the seed value ('-s').
-s seed
Random number seed for the cross-validation and percentage split (default: 1).
-m filename
The name of a file containing a cost matrix.
-l filename
Loads classifier from the given file. In case the filename ends with ".xml",a PMML file is loaded or, if that fails, options are loaded from XML.
-d filename
Saves classifier built from the training data into the given file. In case the filename ends with ".xml" the options are saved XML, not the model.
-v
Outputs no statistics for the training data.
-o
Outputs statistics only, not the classifier.
-i
Outputs detailed information-retrieval statistics per class.
-k
Outputs information-theoretic statistics.
-classifications "weka.classifiers.evaluation.output.prediction.AbstractOutput + options"
Uses the specified class for generating the classification output. E.g.: weka.classifiers.evaluation.output.prediction.PlainText or : weka.classifiers.evaluation.output.prediction.CSV -p range
Outputs predictions for test instances (or the train instances if no test instances provided and -no-cv is used), along with the attributes in the specified range (and nothing else). Use '-p 0' if no attributes are desired.
Deprecated: use "-classifications ..." instead.
-distribution
Outputs the distribution instead of only the prediction in conjunction with the '-p' option (only nominal classes).
Deprecated: use "-classifications ..." instead.
-r
Outputs cumulative margin distribution (and nothing else).
-g
Only for classifiers that implement "Graphable." Outputs the graph representation of the classifier (and nothing else).
-xml filename | xml-string
Retrieves the options from the XML-data instead of the command line.
-threshold-file file
The file to save the threshold data to. The format is determined by the extensions, e.g., '.arff' for ARFF format or '.csv' for CSV.
-threshold-label label
The class label to determine the threshold data for (default is the first label)
@param classifierString class of machine learning classifier as a string @param options the array of string containing the options @throws Exception if model could not be evaluated successfully @return a string describing the results

Examples of weka.classifiers.Evaluation.evaluateModel()

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


      //update errors/alphas
      treeErrors();

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Examples of weka.classifiers.Evaluation.evaluateModel()

    
  Evaluation eval = new Evaluation(m_train);
    
  if (!m_isLeaf) {
      m_isLeaf = true;
      eval.evaluateModel(this, m_train);
      m_isLeaf = false;
      m_numIncorrectModel = eval.incorrect();
      for (int i = 0; i < m_sons.length; i++) m_sons[i].modelErrors();
  } else {
      eval.evaluateModel(this, m_train);

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Examples of weka.classifiers.Evaluation.evaluateModel()

      eval.evaluateModel(this, m_train);
      m_isLeaf = false;
      m_numIncorrectModel = eval.incorrect();
      for (int i = 0; i < m_sons.length; i++) m_sons[i].modelErrors();
  } else {
      eval.evaluateModel(this, m_train);
      m_numIncorrectModel = eval.incorrect();
  }
    }
    
    /**

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Examples of weka.classifiers.Evaluation.evaluateModel()

     * @return the error rate
     * @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.

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Examples of weka.classifiers.Evaluation.evaluateModel()

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


    /**
     * Helper function to find the minimum in an array of error values.

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Examples of weka.classifiers.Evaluation.evaluateModel()

     */
    public void errorAnalysis(String attribute) throws Exception {
        readModel();
        _logger.info("Performing error analysis");
        Evaluation eval = new Evaluation(_testInstances);
        eval.evaluateModel(_cls, _testInstances);
        _predictionList = new HashMap<String, List<Prediction>>();
        String predicted, actual = null;
        double[] distribution = null;
        _predictionList.put(_config._truePositives, new ArrayList<Prediction>());
        _predictionList.put(_config._trueNegatives, new ArrayList<Prediction>());

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Examples of weka.classifiers.Evaluation.evaluateModel()

     */
    public void evaluate() throws Exception {
        readModel();
        _logger.info("Classifying with " + _config._classifier);
        Evaluation eval = new Evaluation(_testInstances);
        eval.evaluateModel(_cls, _testInstances);
        _logger.info("\n" + eval.toSummaryString());
        try {
            _logger.info("\n" + eval.toClassDetailsString());
        } catch (Exception e) {
            _logger.info("Can not create class details" + _config._classifier);

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Examples of weka.classifiers.Evaluation.evaluateModel()

               
            // Build the classifier
            filteredClassifier.buildClassifier(train);
             
            // Evaluate
            eval.evaluateModel(classifier, test);
            
            // Add predictions
            AddClassification filter = new AddClassification();
            filter.setClassifier(classifier);
            filter.setOutputClassification(true);

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Examples of weka.classifiers.Evaluation.evaluateModel()

             
          // Build the classifier
          filteredClassifier.buildClassifier(train);
           
          // Evaluate
          eval.evaluateModel(filteredClassifier, test);
          
          // Add predictions
          AddClassification filter = new AddClassification();
          filter.setClassifier(filteredClassifier);
          filter.setOutputClassification(true);

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Examples of weka.classifiers.Evaluation.evaluateModel()

               
            // Build the classifier
            filteredClassifier.buildClassifier(train);
             
            // Evaluate
            eval.evaluateModel(classifier, test);
            
            // Add predictions
            AddClassification filter = new AddClassification();
            filter.setClassifier(classifier);
            filter.setOutputClassification(true);

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