org.tribuo.regression.sgd

Interface RegressionObjective

All Superinterfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>, SGDObjective<DenseVector>

All Known Implementing Classes:

AbsoluteLoss, Huber, SquaredLoss

public interface RegressionObjective
extends SGDObjective<DenseVector>

An interface for regression objectives.

Method Summary

Modifier and Type	Method and Description
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	loss(DenseVector truth, SGDVector prediction) Deprecated. In 4.1 to move to the new name, lossAndGradient.
default com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	lossAndGradient(DenseVector truth, SGDVector prediction) Scores a prediction, returning the loss and a vector of per output dimension gradients.

Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Configurable

postConfig

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

getProvenance

Method Detail

loss

@Deprecated
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector> loss(DenseVector truth,
                                                                        SGDVector prediction)

Deprecated. In 4.1 to move to the new name, lossAndGradient.

Scores a prediction, returning the loss.

Parameters:

truth - The true regression value.

prediction - The predicted regression value.

Returns:

A pair with the loss and gradient.

lossAndGradient

default com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector> lossAndGradient(DenseVector truth,
                                                                               SGDVector prediction)

Description copied from interface: SGDObjective

Scores a prediction, returning the loss and a vector of per output dimension gradients.

Specified by:

lossAndGradient in interface SGDObjective<DenseVector>

Parameters:

truth - The true output.

prediction - The prediction for each dimension.

Returns:

The score and per dimension gradient.