org.tribuo.regression.evaluation

Enum RegressionMetrics

java.lang.Object

java.lang.Enum<RegressionMetrics>

org.tribuo.regression.evaluation.RegressionMetrics

All Implemented Interfaces:

Serializable, Comparable<RegressionMetrics>

public enum RegressionMetrics
extends Enum<RegressionMetrics>

An enum of the default RegressionMetrics supported by the multi-dimensional regression evaluation package.

The metrics treat each regressed dimension independently.

Enum Constant Summary

Enum Constants

Enum Constant and Description
EV Calculates the Explained Variance of the predictions.
MAE Calculates the Mean Absolute Error of the predictions.
R2 Calculates the R^2 of the predictions.
RMSE Calculates the Root Mean Squared Error of the predictions.

Method Summary

Modifier and Type	Method and Description
static double	explainedVariance(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates the explained variance based on the supplied statistics.
static double	explainedVariance(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates the explained variance based on the supplied statistics for a single dimension.
static double	mae(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates the Mean Absolute Error based on the supplied statistics.
static double	mae(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates the Mean Absolute Error based on the supplied statistics for a single dimension.
static double	r2(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates R^2 based on the supplied statistics.
static double	r2(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates R^2 based on the supplied statistics for a single dimension.
static double	rmse(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates the RMSE based on the supplied statistics.
static double	rmse(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates the RMSE based on the supplied statistics for a single dimension.
static RegressionMetrics	valueOf(String name) Returns the enum constant of this type with the specified name.
static RegressionMetrics[]	values() Returns an array containing the constants of this enum type, in the order they are declared.

Methods inherited from class java.lang.Enum

clone, compareTo, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Enum Constant Detail

R2

public static final RegressionMetrics R2

Calculates the R^2 of the predictions.

RMSE

public static final RegressionMetrics RMSE

Calculates the Root Mean Squared Error of the predictions.

MAE

public static final RegressionMetrics MAE

Calculates the Mean Absolute Error of the predictions.

EV

public static final RegressionMetrics EV

Calculates the Explained Variance of the predictions.

Method Detail

values

public static RegressionMetrics[] values()

Returns an array containing the constants of this enum type, in the order they are declared. This method may be used to iterate over the constants as follows:

for (RegressionMetrics c : RegressionMetrics.values())
    System.out.println(c);

Returns:

an array containing the constants of this enum type, in the order they are declared

valueOf

public static RegressionMetrics valueOf(String name)

Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)

Parameters:

name - the name of the enum constant to be returned.

Returns:

the enum constant with the specified name

Throws:

IllegalArgumentException - if this enum type has no constant with the specified name

NullPointerException - if the argument is null

r2

public static double r2(MetricTarget<Regressor> target,
                        RegressionSufficientStatistics sufficientStats)

Calculates R^2 based on the supplied statistics.

Parameters:

target - The regression dimension or average to target.

sufficientStats - The sufficient statistics.

Returns:

The R^2 value of the predictions.

r2

public static double r2(Regressor variable,
                        RegressionSufficientStatistics sufficientStats)

Calculates R^2 based on the supplied statistics for a single dimension.

Parameters:

variable - The regression dimension.

sufficientStats - The sufficient statistics.

Returns:

The R^2 value of the predictions.

rmse

public static double rmse(MetricTarget<Regressor> target,
                          RegressionSufficientStatistics sufficientStats)

Calculates the RMSE based on the supplied statistics.

Parameters:

target - The regression dimension or average to target.

sufficientStats - The sufficient statistics.

Returns:

The RMSE of the predictions.

rmse

public static double rmse(Regressor variable,
                          RegressionSufficientStatistics sufficientStats)

Calculates the RMSE based on the supplied statistics for a single dimension.

Parameters:

variable - The regression dimension to target.

sufficientStats - The sufficient statistics.

Returns:

The RMSE of the predictions.

mae

public static double mae(MetricTarget<Regressor> target,
                         RegressionSufficientStatistics sufficientStats)

Calculates the Mean Absolute Error based on the supplied statistics.

Parameters:

target - The regression dimension or average to target.

sufficientStats - The sufficient statistics.

Returns:

The MAE of the predictions.

mae

public static double mae(Regressor variable,
                         RegressionSufficientStatistics sufficientStats)

Calculates the Mean Absolute Error based on the supplied statistics for a single dimension.

Parameters:

variable - The regression dimension to target.

sufficientStats - The sufficient statistics.

Returns:

The MAE of the predictions.

explainedVariance

public static double explainedVariance(MetricTarget<Regressor> target,
                                       RegressionSufficientStatistics sufficientStats)

Calculates the explained variance based on the supplied statistics.

Parameters:

target - The regression dimension or average to target.

sufficientStats - The sufficient statistics.

Returns:

The explained variance of the truth given the predictions.

explainedVariance

public static double explainedVariance(Regressor variable,
                                       RegressionSufficientStatistics sufficientStats)

Calculates the explained variance based on the supplied statistics for a single dimension.

Parameters:

variable - The regression dimension to target.

sufficientStats - The sufficient statistics.

Returns:

The explained variance of the truth given the predictions.