weka.classifiers.evaluation

## Class Evaluation

- java.lang.Object
- - weka.classifiers.evaluation.Evaluation

- All Implemented Interfaces:
    java.io.Serializable, RevisionHandler, Summarizable
  Direct Known Subclasses:
    AggregateableEvaluation

      public class Evaluation
      extends java.lang.Object
      implements Summarizable, RevisionHandler, java.io.Serializable

  Class for evaluating machine learning models.

  -------------------------------------------------------------------

  General options when evaluating a learning scheme from the command-line:

  -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.

  -disable list
  A comma separated list of metric names not to include in the output.

  -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.

  -output-models-for-training-splits
  Output models for training splits if cross-validation or percentage-split evaluation is used.

  -do-not-output-per-class-statistics
  Do not output 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.

  -no-predictions
  Turns off the collection of predictions in order to conserve memory.

  -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)

  -------------------------------------------------------------------

  Example usage as the main of a classifier (called FunkyClassifier): `

       public static void main(String [] args) {
         runClassifier(new FunkyClassifier(), args);
       }

  `

  ------------------------------------------------------------------

  Example usage from within an application: `

       Instances trainInstances = ... instances got from somewhere
       Instances testInstances = ... instances got from somewhere
       Classifier scheme = ... scheme got from somewhere

       Evaluation evaluation = new Evaluation(trainInstances);
       evaluation.evaluateModel(scheme, testInstances);
       System.out.println(evaluation.toSummaryString());

  `
  Version:
    $Revision: 14756 $
  Author:
    Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
  See Also:
    Serialized Form

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static java.lang.String[]` | `BUILT_IN_EVAL_METRICS`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `Evaluation(Instances data)` Initializes all the counters for the evaluation.
    `Evaluation(Instances data, CostMatrix costMatrix)` Initializes all the counters for the evaluation and also takes a cost matrix as parameter.

  - ### Method Summary

    All Methods Static Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `double` | `areaUnderPRC(int classIndex)` Returns the area under precision-recall curve (AUPRC) for those predictions that have been collected in the evaluateClassifier(Classifier, Instances) method.
    `double` | `areaUnderROC(int classIndex)` Returns the area under ROC for those predictions that have been collected in the evaluateClassifier(Classifier, Instances) method.
    `double` | `avgCost()` Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
    `double[][]` | `confusionMatrix()` Returns a copy of the confusion matrix.
    `double` | `correct()` Gets the number of instances correctly classified (that is, for which a correct prediction was made).
    `double` | `correlationCoefficient()` Returns the correlation coefficient if the class is numeric.
    `double` | `coverageOfTestCasesByPredictedRegions()` Gets the coverage of the test cases by the predicted regions at the confidence level specified when evaluation was performed.
    `void` | `crossValidateModel(Classifier classifier, Instances data, int numFolds, java.util.Random random)` Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
    `void` | `crossValidateModel(Classifier classifier, Instances data, int numFolds, java.util.Random random, java.lang.Object... forPrinting)` Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
    `void` | `crossValidateModel(java.lang.String classifierString, Instances data, int numFolds, java.lang.String[] options, java.util.Random random)` Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
    `boolean` | `equals(java.lang.Object obj)` Tests whether the current evaluation object is equal to another evaluation object.
    `double` | `errorRate()` Returns the estimated error rate or the root mean squared error (if the class is numeric).
    `double[]` | `evaluateModel(Classifier classifier, Instances data, java.lang.Object... forPredictionsPrinting)` Evaluates the classifier on a given set of instances.
    `static java.lang.String` | `evaluateModel(Classifier classifier, java.lang.String[] options)` Evaluates a classifier with the options given in an array of strings.
    `static java.lang.String` | `evaluateModel(java.lang.String classifierString, java.lang.String[] options)` Evaluates a classifier with the options given in an array of strings.
    `double` | `evaluateModelOnce(Classifier classifier, Instance instance)` Evaluates the classifier on a single instance.
    `double` | `evaluateModelOnce(double[] dist, Instance instance)` Evaluates the supplied distribution on a single instance.
    `void` | `evaluateModelOnce(double prediction, Instance instance)` Evaluates the supplied prediction on a single instance.
    `double` | `evaluateModelOnceAndRecordPrediction(Classifier classifier, Instance instance)` Evaluates the classifier on a single instance and records the prediction.
    `double` | `evaluateModelOnceAndRecordPrediction(double[] dist, Instance instance)` Evaluates the supplied distribution on a single instance.
    `double` | `evaluationForSingleInstance(double[] dist, Instance instance, boolean storePredictions)` Evaluates the supplied distribution on a single instance.
    `double` | `falseNegativeRate(int classIndex)` Calculate the false negative rate with respect to a particular class.
    `double` | `falsePositiveRate(int classIndex)` Calculate the false positive rate with respect to a particular class.
    `double` | `fMeasure(int classIndex)` Calculate the F-Measure with respect to a particular class.
    `static java.util.List<java.lang.String>` | `getAllEvaluationMetricNames()` Utility method to get a list of the names of all built-in and plugin evaluation metrics
    `double[]` | `getClassPriors()` Get the current weighted class counts.
    `boolean` | `getDiscardPredictions()` Returns whether predictions are not recorded at all, in order to conserve memory.
    `Instances` | `getHeader()` Returns the header of the underlying dataset.
    `java.util.List<java.lang.String>` | `getMetricsToDisplay()` Get a list of the names of metrics to have appear in the output The default is to display all built in metrics and plugin metrics that haven't been globally disabled.
    `AbstractEvaluationMetric` | `getPluginMetric(java.lang.String name)` Get the named plugin evaluation metric
    `java.util.List<AbstractEvaluationMetric>` | `getPluginMetrics()` Returns the list of plugin metrics in use (or null if there are none)
    `java.lang.String` | `getRevision()` Returns the revision string.
    `double` | `incorrect()` Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
    `double` | `kappa()` Returns value of kappa statistic if class is nominal.
    `double` | `KBInformation()` Return the total Kononenko & Bratko Information score in bits.
    `double` | `KBMeanInformation()` Return the Kononenko & Bratko Information score in bits per instance.
    `double` | `KBRelativeInformation()` Return the Kononenko & Bratko Relative Information score.
    `static void` | `main(java.lang.String[] args)` A test method for this class.
    `double` | `matthewsCorrelationCoefficient(int classIndex)` Calculates the matthews correlation coefficient (sometimes called phi coefficient) for the supplied class
    `double` | `meanAbsoluteError()` Returns the mean absolute error.
    `double` | `meanPriorAbsoluteError()` Returns the mean absolute error of the prior.
    `double` | `missingClass()` Gets the weight of the instances that had missing class values
    `double` | `numFalseNegatives(int classIndex)` Calculate number of false negatives with respect to a particular class.
    `double` | `numFalsePositives(int classIndex)` Calculate number of false positives with respect to a particular class.
    `double` | `numInstances()` Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
    `double` | `numTrueNegatives(int classIndex)` Calculate the number of true negatives with respect to a particular class.
    `double` | `numTruePositives(int classIndex)` Calculate the number of true positives with respect to a particular class.
    `double` | `pctCorrect()` Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
    `double` | `pctIncorrect()` Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
    `double` | `pctUnclassified()` Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
    `double` | `precision(int classIndex)` Calculate the precision with respect to a particular class.
    `java.util.ArrayList<Prediction>` | `predictions()` Returns the predictions that have been collected.
    `double` | `priorEntropy()` Returns the mean base-2 log loss wrt the null model.
    `double` | `recall(int classIndex)` Calculate the recall with respect to a particular class.
    `double` | `relativeAbsoluteError()` Returns the relative absolute error.
    `double` | `rootMeanPriorSquaredError()` Returns the root mean prior squared error.
    `double` | `rootMeanSquaredError()` Returns the root mean squared error.
    `double` | `rootRelativeSquaredError()` Returns the root relative squared error if the class is numeric.
    `void` | `setDiscardPredictions(boolean value)` Sets whether to discard predictions, ie, not storing them for future reference via predictions() method in order to conserve memory.
    `void` | `setMetricsToDisplay(java.util.List<java.lang.String> display)` Set a list of the names of metrics to have appear in the output.
    `void` | `setPriors(Instances train)` Sets the class prior probabilities.
    `double` | `SFEntropyGain()` Returns the difference in base-2 log loss between null model and scheme.
    `double` | `SFMeanEntropyGain()` Returns the mean difference in base-2 log loss between null model and scheme.
    `double` | `SFMeanPriorEntropy()` Returns the mean base-2 log loss wrt the null model.
    `double` | `SFMeanSchemeEntropy()` Returns the mean base-2 log loss wrt the scheme.
    `double` | `SFPriorEntropy()` Returns the base-2 log loss wrt the null model.
    `double` | `SFSchemeEntropy()` Returns the base-2 log loss wrt the scheme.
    `double` | `sizeOfPredictedRegions()` Gets the average size of the predicted regions, relative to the range of the target in the training data, at the confidence level specified when evaluation was performed.
    `java.lang.String` | `toClassDetailsString()` Generates a breakdown of the accuracy for each class (with default title), incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.
    `java.lang.String` | `toClassDetailsString(java.lang.String title)` Generates a breakdown of the accuracy for each class, incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.
    `java.lang.String` | `toCumulativeMarginDistributionString()` Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
    `void` | `toggleEvalMetrics(java.util.List<java.lang.String> metricsToToggle)` Toggle the output of the metrics specified in the supplied list.
    `java.lang.String` | `toMatrixString()` Calls toMatrixString() with a default title.
    `java.lang.String` | `toMatrixString(java.lang.String title)` Outputs the performance statistics as a classification confusion matrix.
    `java.lang.String` | `toSummaryString()` Calls toSummaryString() with no title and no complexity stats.
    `java.lang.String` | `toSummaryString(boolean printComplexityStatistics)` Calls toSummaryString() with a default title.
    `java.lang.String` | `toSummaryString(java.lang.String title, boolean printComplexityStatistics)` Outputs the performance statistics in summary form.
    `double` | `totalCost()` Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.
    `double` | `trueNegativeRate(int classIndex)` Calculate the true negative rate with respect to a particular class.
    `double` | `truePositiveRate(int classIndex)` Calculate the true positive rate with respect to a particular class.
    `double` | `unclassified()` Gets the number of instances not classified (that is, for which no prediction was made by the classifier).
    `double` | `unweightedMacroFmeasure()` Unweighted macro-averaged F-measure.
    `double` | `unweightedMicroFmeasure()` Unweighted micro-averaged F-measure.
    `void` | `updatePriors(Instance instance)` Updates the class prior probabilities or the mean respectively (when incrementally training).
    `void` | `useNoPriors()` disables the use of priors, e.g., in case of de-serialized schemes that have no access to the original training set, but are evaluated on a set set.
    `double` | `weightedAreaUnderPRC()` Calculates the weighted (by class size) AUPRC.
    `double` | `weightedAreaUnderROC()` Calculates the weighted (by class size) AUC.
    `double` | `weightedFalseNegativeRate()` Calculates the weighted (by class size) false negative rate.
    `double` | `weightedFalsePositiveRate()` Calculates the weighted (by class size) false positive rate.
    `double` | `weightedFMeasure()` Calculates the macro weighted (by class size) average F-Measure.
    `double` | `weightedMatthewsCorrelation()` Calculates the weighted (by class size) matthews correlation coefficient.
    `double` | `weightedPrecision()` Calculates the weighted (by class size) precision.
    `double` | `weightedRecall()` Calculates the weighted (by class size) recall.
    `double` | `weightedTrueNegativeRate()` Calculates the weighted (by class size) true negative rate.
    `double` | `weightedTruePositiveRate()` Calculates the weighted (by class size) true positive rate.
    `static java.lang.String` | `wekaStaticWrapper(Sourcable classifier, java.lang.String className)` Wraps a static classifier in enough source to test using the weka class libraries.
    `double` | `withClass()` Gets the weight of the instances that had a non-missing class value

    - ### Methods inherited from class java.lang.Object

      `getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- - ### Field Detail

    - #### BUILT_IN_EVAL_METRICS

          public static final java.lang.String[] BUILT_IN_EVAL_METRICS

  - ### Constructor Detail

    - #### Evaluation

          public Evaluation(Instances data)
                     throws java.lang.Exception

      Initializes all the counters for the evaluation. Use `useNoPriors()` if the dataset is the test set and you can't initialize with the priors from the training set via `setPriors(Instances)`.
      Parameters:
        `data` - set of training instances, to get some header information and prior class distribution information
      Throws:
        `java.lang.Exception` - if the class is not defined
      See Also:
        `useNoPriors()`, `setPriors(Instances)`

    - #### Evaluation

          public Evaluation(Instances data,
                            CostMatrix costMatrix)
                     throws java.lang.Exception

      Initializes all the counters for the evaluation and also takes a cost matrix as parameter. Use `useNoPriors()` if the dataset is the test set and you can't initialize with the priors from the training set via `setPriors(Instances)`.
      Parameters:
        `data` - set of training instances, to get some header information and prior class distribution information
        `costMatrix` - the cost matrix---if null, default costs will be used
      Throws:
        `java.lang.Exception` - if cost matrix is not compatible with data, the class is not defined or the class is numeric
      See Also:
        `useNoPriors()`, `setPriors(Instances)`

  - ### Method Detail

    - #### getAllEvaluationMetricNames

          public static java.util.List<java.lang.String> getAllEvaluationMetricNames()

      Utility method to get a list of the names of all built-in and plugin evaluation metrics
      Returns:
        the complete list of available evaluation metrics

    - #### getHeader

          public Instances getHeader()

      Returns the header of the underlying dataset.
      Returns:
        the header information

    - #### setDiscardPredictions

          public void setDiscardPredictions(boolean value)

      Sets whether to discard predictions, ie, not storing them for future reference via predictions() method in order to conserve memory.
      Parameters:
        `value` - true if to discard the predictions
      See Also:
        `predictions()`

    - #### getDiscardPredictions

          public boolean getDiscardPredictions()

      Returns whether predictions are not recorded at all, in order to conserve memory.
      Returns:
        true if predictions are not recorded
      See Also:
        `predictions()`

    - #### getPluginMetrics

          public java.util.List<AbstractEvaluationMetric> getPluginMetrics()

      Returns the list of plugin metrics in use (or null if there are none)
      Returns:
        the list of plugin metrics

    - #### setMetricsToDisplay

          public void setMetricsToDisplay(java.util.List<java.lang.String> display)

      Set a list of the names of metrics to have appear in the output. The default is to display all built in metrics and plugin metrics that haven't been globally disabled.
      Parameters:
        `display` - a list of metric names to have appear in the output

    - #### getMetricsToDisplay

          public java.util.List<java.lang.String> getMetricsToDisplay()

      Get a list of the names of metrics to have appear in the output The default is to display all built in metrics and plugin metrics that haven't been globally disabled.
      Returns:
        a list of metric names to have appear in the output

    - #### toggleEvalMetrics

          public void toggleEvalMetrics(java.util.List<java.lang.String> metricsToToggle)

      Toggle the output of the metrics specified in the supplied list.
      Parameters:
        `metricsToToggle` - a list of metrics to toggle

    - #### getPluginMetric

          public AbstractEvaluationMetric getPluginMetric(java.lang.String name)

      Get the named plugin evaluation metric
      Parameters:
        `name` - the name of the metric (as returned by AbstractEvaluationMetric.getName()) or the fully qualified class name of the metric to find
      Returns:
        the metric or null if the metric is not in the list of plugin metrics

    - #### areaUnderROC

          public double areaUnderROC(int classIndex)

      Returns the area under ROC for those predictions that have been collected in the evaluateClassifier(Classifier, Instances) method. Returns Utils.missingValue() if the area is not available.
      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the area under the ROC curve or not a number

    - #### weightedAreaUnderROC

          public double weightedAreaUnderROC()

      Calculates the weighted (by class size) AUC.
      Returns:
        the weighted AUC.

    - #### areaUnderPRC

          public double areaUnderPRC(int classIndex)

      Returns the area under precision-recall curve (AUPRC) for those predictions that have been collected in the evaluateClassifier(Classifier, Instances) method. Returns Utils.missingValue() if the area is not available.
      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the area under the precision-recall curve or not a number

    - #### weightedAreaUnderPRC

          public double weightedAreaUnderPRC()

      Calculates the weighted (by class size) AUPRC.
      Returns:
        the weighted AUPRC.

    - #### confusionMatrix

          public double[][] confusionMatrix()

      Returns a copy of the confusion matrix.
      Returns:
        a copy of the confusion matrix as a two-dimensional array

    - #### crossValidateModel

          public void crossValidateModel(Classifier classifier,
                                         Instances data,
                                         int numFolds,
                                         java.util.Random random)
                                  throws java.lang.Exception

      Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances. Now performs a deep copy of the classifier before each call to buildClassifier() (just in case the classifier is not initialized properly).
      Parameters:
        `classifier` - the classifier with any options set.
        `data` - the data on which the cross-validation is to be performed
        `numFolds` - the number of folds for the cross-validation
        `random` - random number generator for randomization
      Throws:
        `java.lang.Exception` - if a classifier could not be generated successfully or the class is not defined

    - #### crossValidateModel

          public void crossValidateModel(Classifier classifier,
                                         Instances data,
                                         int numFolds,
                                         java.util.Random random,
                                         java.lang.Object... forPrinting)
                                  throws java.lang.Exception

      Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances. Performs a deep copy of the classifier before each call to buildClassifier() (just in case the classifier is not initialized properly).
      Parameters:
        `classifier` - the classifier with any options set.
        `data` - the data on which the cross-validation is to be performed
        `numFolds` - the number of folds for the cross-validation
        `random` - random number generator for randomization
        `forPrinting` - varargs parameter that, if supplied, is expected to hold a weka.classifiers.evaluation.output.prediction.AbstractOutput object or a StringBuffer for model output
      Throws:
        `java.lang.Exception` - if a classifier could not be generated successfully or the class is not defined

    - #### crossValidateModel

          public void crossValidateModel(java.lang.String classifierString,
                                         Instances data,
                                         int numFolds,
                                         java.lang.String[] options,
                                         java.util.Random random)
                                  throws java.lang.Exception

      Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
      Parameters:
        `classifierString` - a string naming the class of the classifier
        `data` - the data on which the cross-validation is to be performed
        `numFolds` - the number of folds for the cross-validation
        `options` - the options to the classifier. Any options
        `random` - the random number generator for randomizing the data accepted by the classifier will be removed from this array.
      Throws:
        `java.lang.Exception` - if a classifier could not be generated successfully or the class is not defined

    - #### evaluateModel

          public static java.lang.String evaluateModel(java.lang.String classifierString,
                                                       java.lang.String[] options)
                                                throws java.lang.Exception

      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.

      -output-models-for-training-splits
      Output models for training splits if cross-validation or percentage-split evaluation is used.

      -do-not-output-per-class-statistics
      Do not output 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.

      -no-predictions
      Turns off the collection of predictions in order to conserve memory.

      -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)

      Parameters:
        `classifierString` - class of machine learning classifier as a string
        `options` - the array of string containing the options
      Returns:
        a string describing the results
      Throws:
        `java.lang.Exception` - if model could not be evaluated successfully

    - #### main

          public static void main(java.lang.String[] args)

      A test method for this class. Just extracts the first command line argument as a classifier class name and calls evaluateModel.
      Parameters:
        `args` - an array of command line arguments, the first of which must be the class name of a classifier.

    - #### evaluateModel

          public static java.lang.String evaluateModel(Classifier classifier,
                                                       java.lang.String[] options)
                                                throws java.lang.Exception

      Evaluates a classifier with the options given in an array of strings.

      Valid options are:

      -t name of training file
      Name of the file with the training data. (required)

      -T name of test file
      Name of the file with the test data. If missing a cross-validation is performed.

      -c class index
      Index of the class attribute (1, 2, ...; default: last).

      -x number of folds
      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 file with cost matrix
      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.

      -output-models-for-training-splits
      Output models for training splits if cross-validation or percentage-split evaluation is used.

      -do-not-output-per-class-statistics
      Do not output 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.

      -no-predictions
      Turns off the collection of predictions in order to conserve memory.

      -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.

      Parameters:
        `classifier` - machine learning classifier
        `options` - the array of string containing the options
      Returns:
        a string describing the results
      Throws:
        `java.lang.Exception` - if model could not be evaluated successfully

    - #### evaluateModel

          public double[] evaluateModel(Classifier classifier,
                                        Instances data,
                                        java.lang.Object... forPredictionsPrinting)
                                 throws java.lang.Exception

      Evaluates the classifier on a given set of instances. Note that the data must have exactly the same format (e.g. order of attributes) as the data used to train the classifier! Otherwise the results will generally be meaningless.
      Parameters:
        `classifier` - machine learning classifier
        `data` - set of test instances for evaluation
        `forPredictionsPrinting` - varargs parameter that, if supplied, is expected to hold a weka.classifiers.evaluation.output.prediction.AbstractOutput object
      Returns:
        the predictions
      Throws:
        `java.lang.Exception` - if model could not be evaluated successfully

    - #### evaluationForSingleInstance

          public double evaluationForSingleInstance(double[] dist,
                                                    Instance instance,
                                                    boolean storePredictions)
                                             throws java.lang.Exception

      Evaluates the supplied distribution on a single instance.
      Parameters:
        `dist` - the supplied distribution
        `instance` - the test instance to be classified
        `storePredictions` - whether to store predictions for nominal classifier
      Returns:
        the prediction
      Throws:
        `java.lang.Exception` - if model could not be evaluated successfully

    - #### evaluateModelOnceAndRecordPrediction

          public double evaluateModelOnceAndRecordPrediction(Classifier classifier,
                                                             Instance instance)
                                                      throws java.lang.Exception

      Evaluates the classifier on a single instance and records the prediction.
      Parameters:
        `classifier` - machine learning classifier
        `instance` - the test instance to be classified
      Returns:
        the prediction made by the clasifier
      Throws:
        `java.lang.Exception` - if model could not be evaluated successfully or the data contains string attributes

    - #### evaluateModelOnce

          public double evaluateModelOnce(Classifier classifier,
                                          Instance instance)
                                   throws java.lang.Exception

      Evaluates the classifier on a single instance.
      Parameters:
        `classifier` - machine learning classifier
        `instance` - the test instance to be classified
      Returns:
        the prediction made by the clasifier
      Throws:
        `java.lang.Exception` - if model could not be evaluated successfully or the data contains string attributes

    - #### evaluateModelOnce

          public double evaluateModelOnce(double[] dist,
                                          Instance instance)
                                   throws java.lang.Exception

      Evaluates the supplied distribution on a single instance.
      Parameters:
        `dist` - the supplied distribution
        `instance` - the test instance to be classified
      Returns:
        the prediction
      Throws:
        `java.lang.Exception` - if model could not be evaluated successfully

    - #### evaluateModelOnceAndRecordPrediction

          public double evaluateModelOnceAndRecordPrediction(double[] dist,
                                                             Instance instance)
                                                      throws java.lang.Exception

      Evaluates the supplied distribution on a single instance.
      Parameters:
        `dist` - the supplied distribution
        `instance` - the test instance to be classified
      Returns:
        the prediction
      Throws:
        `java.lang.Exception` - if model could not be evaluated successfully

    - #### evaluateModelOnce

          public void evaluateModelOnce(double prediction,
                                        Instance instance)
                                 throws java.lang.Exception

      Evaluates the supplied prediction on a single instance.
      Parameters:
        `prediction` - the supplied prediction
        `instance` - the test instance to be classified
      Throws:
        `java.lang.Exception` - if model could not be evaluated successfully

    - #### predictions

          public java.util.ArrayList<Prediction> predictions()

      Returns the predictions that have been collected.
      Returns:
        a reference to the FastVector containing the predictions that have been collected. This should be null if no predictions have been collected.

    - #### wekaStaticWrapper

          public static java.lang.String wekaStaticWrapper(Sourcable classifier,
                                                           java.lang.String className)
                                                    throws java.lang.Exception

      Wraps a static classifier in enough source to test using the weka class libraries.
      Parameters:
        `classifier` - a Sourcable Classifier
        `className` - the name to give to the source code class
      Returns:
        the source for a static classifier that can be tested with weka libraries.
      Throws:
        `java.lang.Exception` - if code-generation fails

    - #### numInstances

          public final double numInstances()

      Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
      Returns:
        the number of test instances with known class

    - #### coverageOfTestCasesByPredictedRegions

          public final double coverageOfTestCasesByPredictedRegions()

      Gets the coverage of the test cases by the predicted regions at the confidence level specified when evaluation was performed.
      Returns:
        the coverage of the test cases by the predicted regions

    - #### sizeOfPredictedRegions

          public final double sizeOfPredictedRegions()

      Gets the average size of the predicted regions, relative to the range of the target in the training data, at the confidence level specified when evaluation was performed.
      Returns:
        the average size of the predicted regions

    - #### withClass

          public final double withClass()

      Gets the weight of the instances that had a non-missing class value
      Returns:
        the weight of the instances that had a non-missing class value

    - #### missingClass

          public final double missingClass()

      Gets the weight of the instances that had missing class values
      Returns:
        the weight of the instances that had missing class values

    - #### incorrect

          public final double incorrect()

      Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made). (Actually the sum of the weights of these instances)
      Returns:
        the number of incorrectly classified instances

    - #### pctIncorrect

          public final double pctIncorrect()

      Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
      Returns:
        the percent of incorrectly classified instances (between 0 and 100)

    - #### totalCost

          public final double totalCost()

      Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.
      Returns:
        the total cost

    - #### avgCost

          public final double avgCost()

      Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
      Returns:
        the average cost.

    - #### correct

          public final double correct()

      Gets the number of instances correctly classified (that is, for which a correct prediction was made). (Actually the sum of the weights of these instances)
      Returns:
        the number of correctly classified instances

    - #### pctCorrect

          public final double pctCorrect()

      Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
      Returns:
        the percent of correctly classified instances (between 0 and 100)

    - #### unclassified

          public final double unclassified()

      Gets the number of instances not classified (that is, for which no prediction was made by the classifier). (Actually the sum of the weights of these instances)
      Returns:
        the number of unclassified instances

    - #### pctUnclassified

          public final double pctUnclassified()

      Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
      Returns:
        the percent of unclassified instances (between 0 and 100)

    - #### errorRate

          public final double 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.
      Returns:
        the estimated error rate (between 0 and 1, or between 0 and maximum cost)

    - #### kappa

          public final double kappa()

      Returns value of kappa statistic if class is nominal.
      Returns:
        the value of the kappa statistic

    - #### correlationCoefficient

          public final double correlationCoefficient()
                                              throws java.lang.Exception

      Returns the correlation coefficient if the class is numeric.
      Returns:
        the correlation coefficient
      Throws:
        `java.lang.Exception` - if class is not numeric

    - #### meanAbsoluteError

          public final double meanAbsoluteError()

      Returns the mean absolute error. Refers to the error of the predicted values for numeric classes, and the error of the predicted probability distribution for nominal classes.
      Returns:
        the mean absolute error

    - #### meanPriorAbsoluteError

          public final double meanPriorAbsoluteError()

      Returns the mean absolute error of the prior.
      Returns:
        the mean absolute error

    - #### relativeAbsoluteError

          public final double relativeAbsoluteError()
                                             throws java.lang.Exception

      Returns the relative absolute error.
      Returns:
        the relative absolute error
      Throws:
        `java.lang.Exception` - if it can't be computed

    - #### rootMeanSquaredError

          public final double rootMeanSquaredError()

      Returns the root mean squared error.
      Returns:
        the root mean squared error

    - #### rootMeanPriorSquaredError

          public final double rootMeanPriorSquaredError()

      Returns the root mean prior squared error.
      Returns:
        the root mean prior squared error

    - #### rootRelativeSquaredError

          public final double rootRelativeSquaredError()

      Returns the root relative squared error if the class is numeric.
      Returns:
        the root relative squared error

    - #### priorEntropy

          public final double priorEntropy()

      Returns the mean base-2 log loss wrt the null model. Just calls SFMeanPriorEntropy.
      Returns:
        the null model entropy per instance

    - #### KBInformation

          public final double KBInformation()
                                     throws java.lang.Exception

      Return the total Kononenko & Bratko Information score in bits.
      Returns:
        the K&B information score
      Throws:
        `java.lang.Exception` - if the class is not nominal

    - #### KBMeanInformation

          public final double KBMeanInformation()
                                         throws java.lang.Exception

      Return the Kononenko & Bratko Information score in bits per instance.
      Returns:
        the K&B information score
      Throws:
        `java.lang.Exception` - if the class is not nominal

    - #### KBRelativeInformation

          public final double KBRelativeInformation()
                                             throws java.lang.Exception

      Return the Kononenko & Bratko Relative Information score. Differs slightly from the expression used in KB's paper because it uses the mean log-loss of the TEST instances wrt to the null model for normalization.
      Returns:
        the K&B relative information score
      Throws:
        `java.lang.Exception` - if the class is not nominal

    - #### SFPriorEntropy

          public final double SFPriorEntropy()

      Returns the base-2 log loss wrt the null model.
      Returns:
        the total null model entropy

    - #### SFMeanPriorEntropy

          public final double SFMeanPriorEntropy()

      Returns the mean base-2 log loss wrt the null model.
      Returns:
        the null model entropy per instance

    - #### SFSchemeEntropy

          public final double SFSchemeEntropy()

      Returns the base-2 log loss wrt the scheme.
      Returns:
        the total scheme entropy

    - #### SFMeanSchemeEntropy

          public final double SFMeanSchemeEntropy()

      Returns the mean base-2 log loss wrt the scheme.
      Returns:
        the scheme entropy per instance

    - #### SFEntropyGain

          public final double SFEntropyGain()

      Returns the difference in base-2 log loss between null model and scheme.
      Returns:
        the total "SF score"

    - #### SFMeanEntropyGain

          public final double SFMeanEntropyGain()

      Returns the mean difference in base-2 log loss between null model and scheme.
      Returns:
        the "SF score" per instance

    - #### toCumulativeMarginDistributionString

          public java.lang.String toCumulativeMarginDistributionString()
                                                                throws java.lang.Exception

      Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
      Returns:
        the cumulative margin distribution
      Throws:
        `java.lang.Exception` - if the class attribute is nominal

    - #### toSummaryString

          public java.lang.String toSummaryString()

      Calls toSummaryString() with no title and no complexity stats.
      Specified by:
        `toSummaryString` in interface `Summarizable`
      Returns:
        a summary description of the classifier evaluation

    - #### toSummaryString

          public java.lang.String toSummaryString(boolean printComplexityStatistics)

      Calls toSummaryString() with a default title.
      Parameters:
        `printComplexityStatistics` - if true, complexity statistics are returned as well
      Returns:
        the summary string

    - #### toSummaryString

          public java.lang.String toSummaryString(java.lang.String title,
                                                  boolean printComplexityStatistics)

      Outputs the performance statistics in summary form. Lists number (and percentage) of instances classified correctly, incorrectly and unclassified. Outputs the total number of instances classified, and the number of instances (if any) that had no class value provided.
      Parameters:
        `title` - the title for the statistics
        `printComplexityStatistics` - if true, complexity statistics are returned as well
      Returns:
        the summary as a String

    - #### toMatrixString

          public java.lang.String toMatrixString()
                                          throws java.lang.Exception

      Calls toMatrixString() with a default title.
      Returns:
        the confusion matrix as a string
      Throws:
        `java.lang.Exception` - if the class is numeric

    - #### toMatrixString

          public java.lang.String toMatrixString(java.lang.String title)
                                          throws java.lang.Exception

      Outputs the performance statistics as a classification confusion matrix. For each class value, shows the distribution of predicted class values.
      Parameters:
        `title` - the title for the confusion matrix
      Returns:
        the confusion matrix as a String
      Throws:
        `java.lang.Exception` - if the class is numeric

    - #### toClassDetailsString

          public java.lang.String toClassDetailsString()
                                                throws java.lang.Exception

      Generates a breakdown of the accuracy for each class (with default title), incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure. Should be useful for ROC curves, recall/precision curves.
      Returns:
        the statistics presented as a string
      Throws:
        `java.lang.Exception` - if class is not nominal

    - #### toClassDetailsString

          public java.lang.String toClassDetailsString(java.lang.String title)
                                                throws java.lang.Exception

      Generates a breakdown of the accuracy for each class, incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure. Should be useful for ROC curves, recall/precision curves.
      Parameters:
        `title` - the title to prepend the stats string with
      Returns:
        the statistics presented as a string
      Throws:
        `java.lang.Exception` - if class is not nominal

    - #### numTruePositives

          public double numTruePositives(int classIndex)

      Calculate the number of true positives with respect to a particular class. This is defined as

           correctly classified positives

      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the true positive rate

    - #### truePositiveRate

          public double truePositiveRate(int classIndex)

      Calculate the true positive rate with respect to a particular class. This is defined as

           correctly classified positives
           ------------------------------
                 total positives

      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the true positive rate

    - #### weightedTruePositiveRate

          public double weightedTruePositiveRate()

      Calculates the weighted (by class size) true positive rate.
      Returns:
        the weighted true positive rate.

    - #### numTrueNegatives

          public double numTrueNegatives(int classIndex)

      Calculate the number of true negatives with respect to a particular class. This is defined as

           correctly classified negatives

      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the true positive rate

    - #### trueNegativeRate

          public double trueNegativeRate(int classIndex)

      Calculate the true negative rate with respect to a particular class. This is defined as

           correctly classified negatives
           ------------------------------
                 total negatives

      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the true positive rate

    - #### weightedTrueNegativeRate

          public double weightedTrueNegativeRate()

      Calculates the weighted (by class size) true negative rate.
      Returns:
        the weighted true negative rate.

    - #### numFalsePositives

          public double numFalsePositives(int classIndex)

      Calculate number of false positives with respect to a particular class. This is defined as

           incorrectly classified negatives

      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the false positive rate

    - #### falsePositiveRate

          public double falsePositiveRate(int classIndex)

      Calculate the false positive rate with respect to a particular class. This is defined as

           incorrectly classified negatives
           --------------------------------
                  total negatives

      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the false positive rate

    - #### weightedFalsePositiveRate

          public double weightedFalsePositiveRate()

      Calculates the weighted (by class size) false positive rate.
      Returns:
        the weighted false positive rate.

    - #### numFalseNegatives

          public double numFalseNegatives(int classIndex)

      Calculate number of false negatives with respect to a particular class. This is defined as

           incorrectly classified positives

      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the false positive rate

    - #### falseNegativeRate

          public double falseNegativeRate(int classIndex)

      Calculate the false negative rate with respect to a particular class. This is defined as

           incorrectly classified positives
           --------------------------------
                  total positives

      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the false positive rate

    - #### weightedFalseNegativeRate

          public double weightedFalseNegativeRate()

      Calculates the weighted (by class size) false negative rate.
      Returns:
        the weighted false negative rate.

    - #### matthewsCorrelationCoefficient

          public double matthewsCorrelationCoefficient(int classIndex)

      Calculates the matthews correlation coefficient (sometimes called phi coefficient) for the supplied class
      Parameters:
        `classIndex` - the index of the class to compute the matthews correlation coefficient for
      Returns:
        the mathews correlation coefficient

    - #### weightedMatthewsCorrelation

          public double weightedMatthewsCorrelation()

      Calculates the weighted (by class size) matthews correlation coefficient.
      Returns:
        the weighted matthews correlation coefficient.

    - #### recall

          public double recall(int classIndex)

      Calculate the recall with respect to a particular class. This is defined as

           correctly classified positives
           ------------------------------
                 total positives

      (Which is also the same as the truePositiveRate.)
      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the recall

    - #### weightedRecall

          public double weightedRecall()

      Calculates the weighted (by class size) recall.
      Returns:
        the weighted recall.

    - #### precision

          public double precision(int classIndex)

      Calculate the precision with respect to a particular class. This is defined as

           correctly classified positives
           ------------------------------
            total predicted as positive

      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the precision

    - #### weightedPrecision

          public double weightedPrecision()

      Calculates the weighted (by class size) precision.
      Returns:
        the weighted precision.

    - #### fMeasure

          public double fMeasure(int classIndex)

      Calculate the F-Measure with respect to a particular class. This is defined as

           2 * recall * precision
           ----------------------
             recall + precision

      Returns zero when both precision and recall are zero
      Parameters:
        `classIndex` - the index of the class to consider as "positive"
      Returns:
        the F-Measure

    - #### weightedFMeasure

          public double weightedFMeasure()

      Calculates the macro weighted (by class size) average F-Measure.
      Returns:
        the weighted F-Measure.

    - #### unweightedMacroFmeasure

          public double unweightedMacroFmeasure()

      Unweighted macro-averaged F-measure. If some classes not present in the test set, they're just skipped (since recall is undefined there anyway) .
      Returns:
        unweighted macro-averaged F-measure.

    - #### unweightedMicroFmeasure

          public double unweightedMicroFmeasure()

      Unweighted micro-averaged F-measure. If some classes not present in the test set, they have no effect. Note: if the test set is *single-label*, then this is the same as accuracy.
      Returns:
        unweighted micro-averaged F-measure.

    - #### setPriors

          public void setPriors(Instances train)
                         throws java.lang.Exception

      Sets the class prior probabilities.
      Parameters:
        `train` - the training instances used to determine the prior probabilities
      Throws:
        `java.lang.Exception` - if the class attribute of the instances is not set

    - #### getClassPriors

          public double[] getClassPriors()

      Get the current weighted class counts.
      Returns:
        the weighted class counts

    - #### updatePriors

          public void updatePriors(Instance instance)
                            throws java.lang.Exception

      Updates the class prior probabilities or the mean respectively (when incrementally training).
      Parameters:
        `instance` - the new training instance seen
      Throws:
        `java.lang.Exception` - if the class of the instance is not set

    - #### useNoPriors

          public void useNoPriors()

      disables the use of priors, e.g., in case of de-serialized schemes that have no access to the original training set, but are evaluated on a set set.

    - #### equals

          public boolean equals(java.lang.Object obj)

      Tests whether the current evaluation object is equal to another evaluation object.
      Overrides:
        `equals` in class `java.lang.Object`
      Parameters:
        `obj` - the object to compare against
      Returns:
        true if the two objects are equal

    - #### getRevision

          public java.lang.String getRevision()

      Returns the revision string.
      Specified by:
        `getRevision` in interface `RevisionHandler`
      Returns:
        the revision