ConfusionMatrix Properties 
The ConfusionMatrix type exposes the following members.
Name  Description  

Accuracy 
Accuracy, or raw performance of the system
 
ActualNegatives 
Gets the number of actual negatives
 
ActualPositives 
Gets the number of actual positives.
 
ChanceAgreement 
Chance agreement.
 
ChiSquare 
Gets the ChiSquare statistic for the contingency table.
 
ColumnTotals 
Gets the marginal sums for table columns.
 
Diagonal 
Gets the diagonal of the confusion matrix.
 
Efficiency 
Efficiency, the arithmetic mean of sensitivity and specificity
 
Error 
Error rate, or 1  accuracy.
 
Errors 
Gets the number of errors between the expected and predicted values.
 
ExpectedValues 
Expected values, or values that could
have been generated just by chance.
 
FalseDiscoveryRate 
False Discovery Rate, or the expected false positive rate.
 
FalseNegatives 
Cases incorrectly identified by the system as negatives.
 
FalsePositiveRate 
False Positive Rate, also known as false alarm rate.
 
FalsePositives 
Cases incorrectly identified by the system as positives.
 
FScore  
GeometricAgreement 
Geometric agreement.
 
Hits 
Gets the number of hits between the expected and predicted values.
 
Kappa 
Kappa coefficient.
 
Matrix 
Gets the confusion matrix in count matrix form.
 
MatthewsCorrelationCoefficient 
Matthews Correlation Coefficient, also known as Phi coefficient
 
NegativePredictiveValue 
Negative Predictive Value, also known as Negative Precision
 
NormalizedMutualInformation 
Normalized Mutual Information.
 
NumberOfClasses 
Gets the number of classes in this decision problem.
 
NumberOfSamples 
Gets the number of observations for this matrix.
 
OddsRatio 
Oddsratio.
 
OverallAgreement 
Overall agreement.
 
OverallDiagnosticPower 
Diagnostic power.
 
Pearson 
Pearson's contingency coefficient C.
 
PositivePredictiveValue 
Positive Predictive Value, also known as Positive Precision
 
Precision 
Precision, same as the PositivePredictiveValue.
 
PredictedNegatives 
Gets the number of predicted negatives.
 
PredictedPositives 
Gets the number of predicted positives.
 
Prevalence 
Prevalence of outcome occurrence.
 
Recall 
Recall, same as the Sensitivity.
 
RowTotals 
Gets the marginal sums for table rows.
 
Samples  Obsolete.
Gets the number of observations for this matrix
 
Sensitivity 
Sensitivity, also known as True Positive Rate
 
Specificity 
Specificity, also known as True Negative Rate
 
StandardError 
Gets the standard error of the Kappa
coefficient of performance.
 
StandardErrorUnderNull 
Gets the standard error of the Kappa
under the null hypothesis that the underlying Kappa
value is 0.
 
TrueNegatives 
Cases correctly identified by the system as negatives.
 
TruePositives 
Cases correctly identified by the system as positives.
 
Variance 
Gets the variance of the Kappa
coefficient of performance.
 
VarianceUnderNull 
Gets the variance of the Kappa
under the null hypothesis that the underlying
Kappa value is 0.
