ReceiverOperatingCharacteristicPoint Class

Accuracy, or raw performance of the system

Gets the number of actual negatives

Gets the number of actual positives.

Chance agreement.

Gets the Chi-Square statistic for the contingency table.

Gets the marginal sums for table columns.

Gets the cutoff value (discrimination threshold) for this point.

Gets the diagonal of the confusion matrix.

Efficiency, the arithmetic mean of sensitivity and specificity

Error rate, or 1 - accuracy.

Gets the number of errors between the expected and predicted values.

Expected values, or values that could have been generated just by chance.

False Discovery Rate, or the expected false positive rate.

Cases incorrectly identified by the system as negatives.

False Positive Rate, also known as false alarm rate.

Cases incorrectly identified by the system as positives.

F-Score, computed as the harmonic mean of Precision and Recall.

Geometric agreement.

Gets the number of hits between the expected and predicted values.

Kappa coefficient.

Gets the confusion matrix in count matrix form.

Matthews Correlation Coefficient, also known as Phi coefficient

Negative Predictive Value, also known as Negative Precision

Normalized Mutual Information.

Gets the number of classes in this decision problem.

Gets the number of observations for this matrix.

Odds-ratio.

Overall agreement.

Diagnostic power.

Pearson's contingency coefficient C.

Positive Predictive Value, also known as Positive Precision

Precision, same as the PositivePredictiveValue.

Gets the number of predicted negatives.

Gets the number of predicted positives.

Prevalence of outcome occurrence.

Recall, same as the Sensitivity.

Gets the marginal sums for table rows.

Gets the number of observations for this matrix

Sensitivity, also known as True Positive Rate

Specificity, also known as True Negative Rate

Gets the standard error of the Kappa coefficient of performance.

Gets the standard error of the Kappa under the null hypothesis that the underlying Kappa value is 0.

Cases correctly identified by the system as negatives.

Cases correctly identified by the system as positives.

Gets the variance of the Kappa coefficient of performance.

Gets the variance of the Kappa under the null hypothesis that the underlying Kappa value is 0.

Determines whether the specified object is equal to the current object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Serves as the default hash function.

Gets the Type of the current instance.

Creates a shallow copy of the current Object.

Converts this matrix into a GeneralConfusionMatrix.

Returns a System.String that represents the current ReceiverOperatingCharacteristicPoint.

Checks whether an object implements a method with the given name.

Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.