Confusion Matrix class More...

Inheritance diagram for CrowdsourcingProject.Statistics.ConfusionMatrix:

Public Member Functions
	ConfusionMatrix (int truePositives, int trueNegatives, int falsePositives, int falseNegatives)
	Constructs a new Confusion Matrix. More...

Properties
int	Observations `[get]`
	Gets the number of observations for this matrix More...

int	ActualPositives `[get]`
	Gets the number of actual positives More...

int	ActualNegatives `[get]`
	Gets the number of actual negatives More...

int	PredictedPositives `[get]`
	Gets the number of predicted positives More...

int	PredictedNegatives `[get]`
	Gets the number of predicted negatives More...

int	TruePositives `[get]`
	Cases correctly identified by the system as positives. More...

int	TrueNegatives `[get]`
	Cases correctly identified by the system as negatives. More...

int	FalsePositives `[get]`
	Cases incorrectly identified by the system as positives. More...

int	FalseNegatives `[get]`
	Cases incorrectly identified by the system as negatives. More...

double	Sensitivity `[get]`
	Sensitivity, also known as True Positive Rate More...

double	Specificity `[get]`
	Specificity, also known as True Negative Rate More...

double	Efficiency `[get]`
	Efficiency, the arithmetic mean of sensitivity and specificity More...

double	Accuracy `[get]`
	Accuracy, or raw performance of the system More...

double	PositivePredictiveValue `[get]`
	Positive Predictive Value, also known as Positive Precision More...

double	NegativePredictiveValue `[get]`
	Negative Predictive Value, also known as Negative Precision More...

double	FalsePositiveRate `[get]`
	False Positive Rate, also known as false alarm rate. More...

double	FalseDiscoveryRate `[get]`
	False Discovery Rate, or the expected false positive rate. More...

double	MatthewsCorrelationCoefficient `[get]`
	Matthews Correlation Coefficient, also known as Phi coefficient More...

Detailed Description

Confusion Matrix class

Constructor & Destructor Documentation

CrowdsourcingProject.Statistics.ConfusionMatrix.ConfusionMatrix	(	int	truePositives,
		int	trueNegatives,
		int	falsePositives,
		int	falseNegatives
	)

inline

Constructs a new Confusion Matrix.

Property Documentation

double CrowdsourcingProject.Statistics.ConfusionMatrix.Accuracy

get

Accuracy, or raw performance of the system

ACC = (TP + TN) / (P + N)

int CrowdsourcingProject.Statistics.ConfusionMatrix.ActualNegatives

get

Gets the number of actual negatives

int CrowdsourcingProject.Statistics.ConfusionMatrix.ActualPositives

get

Gets the number of actual positives

double CrowdsourcingProject.Statistics.ConfusionMatrix.Efficiency

get

Efficiency, the arithmetic mean of sensitivity and specificity

double CrowdsourcingProject.Statistics.ConfusionMatrix.FalseDiscoveryRate

get

False Discovery Rate, or the expected false positive rate.

The False Discovery Rate is actually the expected false positive rate.

For example, if 1000 observations were experimentally predicted to be different, and a maximum FDR for these observations was 0.10, then 100 of these observations would be expected to be false positives.

It is calculated as: FDR = FP / (FP + TP)

int CrowdsourcingProject.Statistics.ConfusionMatrix.FalseNegatives

get

Cases incorrectly identified by the system as negatives.

double CrowdsourcingProject.Statistics.ConfusionMatrix.FalsePositiveRate

get

False Positive Rate, also known as false alarm rate.

It can be calculated as: FPR = FP / (FP + TN) or also as: FPR = (1-specifity)

int CrowdsourcingProject.Statistics.ConfusionMatrix.FalsePositives

get

Cases incorrectly identified by the system as positives.

double CrowdsourcingProject.Statistics.ConfusionMatrix.MatthewsCorrelationCoefficient

get

Matthews Correlation Coefficient, also known as Phi coefficient

A coefficient of +1 represents a perfect prediction, 0 an average random prediction and −1 an inverse prediction.

double CrowdsourcingProject.Statistics.ConfusionMatrix.NegativePredictiveValue

get

Negative Predictive Value, also known as Negative Precision

The Negative Predictive Value tells us how likely it is that the disease is NOT present for a patient, given that the patient's test for the disease is negative.

It can be calculated as: NPV = TN / (TN + FN)

int CrowdsourcingProject.Statistics.ConfusionMatrix.Observations

get

Gets the number of observations for this matrix

double CrowdsourcingProject.Statistics.ConfusionMatrix.PositivePredictiveValue

get

Positive Predictive Value, also known as Positive Precision

The Positive Predictive Value tells us how likely is that a patient has a disease, given that the test for this disease is positive.

It can be calculated as: PPV = TP / (TP + FP)

int CrowdsourcingProject.Statistics.ConfusionMatrix.PredictedNegatives

get

Gets the number of predicted negatives

int CrowdsourcingProject.Statistics.ConfusionMatrix.PredictedPositives

get

Gets the number of predicted positives

double CrowdsourcingProject.Statistics.ConfusionMatrix.Sensitivity

get

Sensitivity, also known as True Positive Rate

Sensitivity = TPR = TP / (TP + FN)

double CrowdsourcingProject.Statistics.ConfusionMatrix.Specificity

get

Specificity, also known as True Negative Rate

Specificity = TNR = TN / (FP + TN) or also as: TNR = (1-False Positive Rate)

int CrowdsourcingProject.Statistics.ConfusionMatrix.TrueNegatives

get

Cases correctly identified by the system as negatives.

int CrowdsourcingProject.Statistics.ConfusionMatrix.TruePositives

get

Cases correctly identified by the system as positives.

The documentation for this class was generated from the following file:

C:/Users/Matteo/Source/Repos/active-crowd/CrowdsourcingModels/Statistics/ConfusionMatrix.cs

Public Member Functions

Properties

Detailed Description

Constructor & Destructor Documentation

Property Documentation