C# 클래스 numl.Supervised.Score

Contains scoring statistics for a given model.
파일 보기 프로젝트 열기: sethjuarez/numl 1 사용 예제들

공개 메소드들

메소드 설명
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.

메소드 상세

CombineScores() 공개 정적인 메소드

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

ComputeCoefRMSE() 공개 정적인 메소드

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.
리턴 double

ComputeMSE() 공개 정적인 메소드

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.
리턴 double

ComputeMeanError() 공개 정적인 메소드

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.
리턴 double

ComputeNormRMSE() 공개 정적인 메소드

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.
리턴 double

ComputeRMSE() 공개 정적인 메소드

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.
리턴 double

ComputeSSE() 공개 정적인 메소드

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.
리턴 double

Score() 공개 메소드

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

ScorePredictions() 공개 정적인 메소드

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.
리턴 Score

ToString() 공개 메소드

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