C# Class numl.Supervised.Score

Contains scoring statistics for a given model.
Afficher le fichier Open project: sethjuarez/numl Class Usage Examples

Méthodes publiques

Méthode Description
CombineScores ( ) : Score

Combines and averages metrics across all the given scores.
ComputeCoefRMSE ( Vector y1, Vector y2 ) : double

Computes the Coefficient of Variation of the Root Mean Squared Error for the given inputs.
ComputeMSE ( Vector y1, Vector y2 ) : double

Computes the Mean Squared Error of the given inputs.
ComputeMeanError ( Vector y1, Vector y2 ) : double

Computes the Mean Absolute Error for the given inputs.
ComputeNormRMSE ( Vector y1, Vector y2 ) : double

Computes the Normalized Root Mean Squared Error for the given inputs.
ComputeRMSE ( Vector y1, Vector y2 ) : double

Computes the Root Mean Squared Error for the given inputs.
ComputeSSE ( Vector y1, Vector y2 ) : double

Computes the Mean Squared Error of the given inputs.
Score ( ) : System

Initializes a new Score object.
ScorePredictions ( Vector predictions, Vector actual, double truthLabel = Ject.DefaultTruthValue, double falseLabel = Ject.DefaultFalseValue ) : Score

Scores a set of predictions against the actual values.
ToString ( ) : string

Returns a string representation of the current Score object.

Method Details

CombineScores() public static méthode

Combines and averages metrics across all the given scores.
public static CombineScores ( ) : Score
Résultat Score

ComputeCoefRMSE() public static méthode

Computes the Coefficient of Variation of the Root Mean Squared Error for the given inputs.
public static ComputeCoefRMSE ( Vector y1, Vector y2 ) : double
y1 numl.Math.LinearAlgebra.Vector Predicted values.
y2 numl.Math.LinearAlgebra.Vector Actual values.
Résultat double

ComputeMSE() public static méthode

Computes the Mean Squared Error of the given inputs.
public static ComputeMSE ( Vector y1, Vector y2 ) : double
y1 numl.Math.LinearAlgebra.Vector Predicted values.
y2 numl.Math.LinearAlgebra.Vector Actual values.
Résultat double

ComputeMeanError() public static méthode

Computes the Mean Absolute Error for the given inputs.
public static ComputeMeanError ( Vector y1, Vector y2 ) : double
y1 numl.Math.LinearAlgebra.Vector Predicted values.
y2 numl.Math.LinearAlgebra.Vector Actual values.
Résultat double

ComputeNormRMSE() public static méthode

Computes the Normalized Root Mean Squared Error for the given inputs.
public static ComputeNormRMSE ( Vector y1, Vector y2 ) : double
y1 numl.Math.LinearAlgebra.Vector Predicted values.
y2 numl.Math.LinearAlgebra.Vector Actual values.
Résultat double

ComputeRMSE() public static méthode

Computes the Root Mean Squared Error for the given inputs.
public static ComputeRMSE ( Vector y1, Vector y2 ) : double
y1 numl.Math.LinearAlgebra.Vector Predicted values.
y2 numl.Math.LinearAlgebra.Vector Actual values.
Résultat double

ComputeSSE() public static méthode

Computes the Mean Squared Error of the given inputs.
public static ComputeSSE ( Vector y1, Vector y2 ) : double
y1 numl.Math.LinearAlgebra.Vector Predicted values.
y2 numl.Math.LinearAlgebra.Vector Actual values.
Résultat double

Score() public méthode

Initializes a new Score object.
public Score ( ) : System
Résultat System

ScorePredictions() public static méthode

Scores a set of predictions against the actual values.
public static ScorePredictions ( Vector predictions, Vector actual, double truthLabel = Ject.DefaultTruthValue, double falseLabel = Ject.DefaultFalseValue ) : Score
predictions numl.Math.LinearAlgebra.Vector Predicted values.
actual numl.Math.LinearAlgebra.Vector Actual values.
truthLabel double (Optional) the truth label in the vector.
falseLabel double (Optional) the false label in the vector.
Résultat Score

ToString() public méthode

Returns a string representation of the current Score object.
public ToString ( ) : string
Résultat string