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m_NumClasses
int m_NumClasses
The number of classes.
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m_NumFolds
int m_NumFolds
The number of folds for a cross-validation.
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m_Incorrect
double m_Incorrect
The weight of all incorrectly classified instances.
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m_Correct
double m_Correct
The weight of all correctly classified instances.
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m_Unclassified
double m_Unclassified
The weight of all unclassified instances.
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m_MissingClass
double m_MissingClass
The weight of all instances that had no class assigned to them.
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m_WithClass
double m_WithClass
The weight of all instances that had a class assigned to them.
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m_ConfusionMatrix
double[][] m_ConfusionMatrix
Array for storing the confusion matrix.
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m_ClassNames
java.lang.String[] m_ClassNames
The names of the classes.
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m_ClassIsNominal
boolean m_ClassIsNominal
Is the class nominal or numeric?
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m_ClassPriors
double[] m_ClassPriors
The prior probabilities of the classes.
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m_ClassPriorsSum
double m_ClassPriorsSum
The sum of counts for priors.
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m_CostMatrix
weka.classifiers.CostMatrix m_CostMatrix
The cost matrix (if given).
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m_TotalCost
double m_TotalCost
The total cost of predictions (includes instance weights).
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m_SumErr
double m_SumErr
Sum of errors.
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m_SumAbsErr
double m_SumAbsErr
Sum of absolute errors.
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m_SumSqrErr
double m_SumSqrErr
Sum of squared errors.
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m_SumClass
double m_SumClass
Sum of class values.
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m_SumSqrClass
double m_SumSqrClass
Sum of squared class values.
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m_SumPredicted
double m_SumPredicted
Sum of predicted values.
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m_SumSqrPredicted
double m_SumSqrPredicted
Sum of squared predicted values.
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m_SumClassPredicted
double m_SumClassPredicted
Sum of predicted * class values.
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m_SumPriorAbsErr
double m_SumPriorAbsErr
Sum of absolute errors of the prior.
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m_SumPriorSqrErr
double m_SumPriorSqrErr
Sum of absolute errors of the prior.
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m_SumKBInfo
double m_SumKBInfo
Total Kononenko & Bratko Information.
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m_MarginCounts
double[] m_MarginCounts
Cumulative margin distribution.
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m_NumTrainClassVals
int m_NumTrainClassVals
Number of non-missing class training instances seen.
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m_TrainClassVals
double[] m_TrainClassVals
Array containing all numeric training class values seen.
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m_TrainClassWeights
double[] m_TrainClassWeights
Array containing all numeric training class weights.
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m_PriorEstimator
weka.estimators.UnivariateKernelEstimator m_PriorEstimator
Numeric class estimator for prior.
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m_ComplexityStatisticsAvailable
boolean m_ComplexityStatisticsAvailable
Whether complexity statistics are available.
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m_SumPriorEntropy
double m_SumPriorEntropy
Total entropy of prior predictions.
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m_SumSchemeEntropy
double m_SumSchemeEntropy
Total entropy of scheme predictions.
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m_CoverageStatisticsAvailable
boolean m_CoverageStatisticsAvailable
Whether coverage statistics are available.
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m_ConfLevel
double m_ConfLevel
The confidence level used for coverage statistics.
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m_TotalSizeOfRegions
double m_TotalSizeOfRegions
Total size of predicted regions at the given confidence level.
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m_TotalCoverage
double m_TotalCoverage
Total coverage of test cases at the given confidence level.
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m_MinTarget
double m_MinTarget
Minimum target value.
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m_MaxTarget
double m_MaxTarget
Maximum target value.
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m_Predictions
java.util.ArrayList<E> m_Predictions
The list of predictions that have been generated (for computing AUC).
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m_NoPriors
boolean m_NoPriors
enables/disables the use of priors, e.g., if no training set is present in
case of de-serialized schemes.
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m_Header
Instances m_Header
The header of the training set.
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m_DiscardPredictions
boolean m_DiscardPredictions
whether to discard predictions (and save memory).
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m_pluginMetrics
java.util.List<E> m_pluginMetrics
Holds plugin evaluation metrics
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m_metricsToDisplay
java.util.List<E> m_metricsToDisplay
The list of metrics to display in the output