org.tribuo.regression.evaluation

Interface RegressionEvaluation

All Superinterfaces:

Evaluation<Regressor>, com.oracle.labs.mlrg.olcut.provenance.Provenancable<EvaluationProvenance>

public interface RegressionEvaluation
extends Evaluation<Regressor>

Defines methods that calculate regression performance.

Method Summary

Modifier and Type	Method and Description
double	averagedExplainedVariance() The average explained variance across all dimensions.
double	averageMAE() The average Mean Absolute Error across all dimensions.
double	averageR2() The average R2 across all dimensions.
double	averageRMSE() The average RMSE across all dimensions.
Map<Regressor,Double>	explainedVariance() Calculatest the explained variance for all dimensions.
double	explainedVariance(Regressor variable) Calculates the explained variance of the ground truth using the predictions for the supplied dimension.
Map<Regressor,Double>	mae() Calculates the Mean Absolute Error for all dimensions.
double	mae(Regressor variable) Calculates the Mean Absolute Error for that dimension.
Map<Regressor,Double>	r2() Calculates R2 for all dimensions.
double	r2(Regressor variable) Calculates R2 for the supplied dimension.
Map<Regressor,Double>	rmse() Calculates the RMSE for all dimensions.
double	rmse(Regressor variable) Calculates the Root Mean Squared Error (i.e., the square root of the average squared errors across all data points) for the supplied dimension.

Methods inherited from interface org.tribuo.evaluation.Evaluation

asMap, get, getPredictions

Methods inherited from interface com.oracle.labs.mlrg.olcut.provenance.Provenancable

getProvenance

Method Detail

averageMAE

double averageMAE()

The average Mean Absolute Error across all dimensions.

Returns:

The average Mean Absolute Error.

mae

double mae(Regressor variable)

Calculates the Mean Absolute Error for that dimension.

Parameters:

variable - The regression dimension to use.

Returns:

The Mean Absolute Error.

mae

Map<Regressor,Double> mae()

Calculates the Mean Absolute Error for all dimensions.

Returns:

The Mean Absolute Error.

averageR2

double averageR2()

The average R2 across all dimensions.

Returns:

The average R2.

r2

double r2(Regressor variable)

Calculates R2 for the supplied dimension.

Parameters:

variable - The regression dimension to use.

Returns:

The R2.

r2

Map<Regressor,Double> r2()

Calculates R2 for all dimensions.

Returns:

The R2.

averageRMSE

double averageRMSE()

The average RMSE across all dimensions.

Returns:

The average RMSE.

rmse

double rmse(Regressor variable)

Calculates the Root Mean Squared Error (i.e., the square root of the average squared errors across all data points) for the supplied dimension.

Parameters:

variable - The regression dimension to use.

Returns:

The RMSE.

rmse

Map<Regressor,Double> rmse()

Calculates the RMSE for all dimensions.

Returns:

The RMSE.

averagedExplainedVariance

double averagedExplainedVariance()

The average explained variance across all dimensions.

Returns:

The average explained variance.

explainedVariance

double explainedVariance(Regressor variable)

Calculates the explained variance of the ground truth using the predictions for the supplied dimension.

Parameters:

variable - The regression dimension to use.

Returns:

The explained variance.

explainedVariance

Map<Regressor,Double> explainedVariance()

Calculatest the explained variance for all dimensions.

Returns:

The explained variance.