Java weka.classifiers Evaluation fields, constructors, methods, implement or subclass

Introduction

In this page you can find the methods, fields and constructors for weka.classifiers Evaluation.

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Subclass

weka.classifiers.Evaluation has subclasses.
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Constructor

	Evaluation(Instances data)

Method

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, 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, Random random, Object... forPredictionsPrinting) Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
void	crossValidateModel(String classifierString, Instances data, int numFolds, String[] options, Random random) Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
double	errorRate() Returns the estimated error rate or the root mean squared error (if the class is numeric).
double[]	evaluateModel(Classifier classifier, Instances data, Object... forPredictionsPrinting) Evaluates the classifier on a given set of instances.
String	evaluateModel(String classifierString, String[] options) Evaluates a classifier with the options given in an array of strings.
String	evaluateModel(Classifier classifier, String[] options) Evaluates a classifier with the options given in an array of strings.
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.
Instances	getHeader() Returns the header of the underlying dataset.
AbstractEvaluationMetric	getPluginMetric(String name) Get the named plugin evaluation metric
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.
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	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.
ArrayList	predictions() Returns the predictions that have been collected.
double	priorEntropy() Calculate the entropy of the prior distribution.
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	setPriors(Instances train) Sets the class prior probabilities.
double	SFEntropyGain() Returns the total SF, which is the null model entropy minus the scheme entropy.
double	SFMeanEntropyGain() Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
double	SFMeanPriorEntropy() Returns the entropy per instance for the null model.
double	SFMeanSchemeEntropy() Returns the entropy per instance for the scheme.
double	SFPriorEntropy() Returns the total entropy for the null model.
double	SFSchemeEntropy() Returns the total entropy for 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.
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.
String	toClassDetailsString(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.
String	toCumulativeMarginDistributionString() Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
String	toMatrixString() Calls toMatrixString() with a default title.
String	toMatrixString(String title) Outputs the performance statistics as a classification confusion matrix.
String	toSummaryString() Calls toSummaryString() with no title and no complexity stats.
String	toSummaryString(String title, boolean printComplexityStatistics) Outputs the performance statistics in summary form.
String	toSummaryString(boolean printComplexityStatistics) Calls toSummaryString() with a default title.
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.
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.