package loss
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object
AbsoluteError extends Loss
Class for absolute error loss calculation (for regression).
Class for absolute error loss calculation (for regression).
The absolute (L1) error is defined as: y  F(x) where y is the label and F(x) is the model prediction for features x.
 Annotations
 @Since( "1.2.0" )

object
LogLoss extends ClassificationLoss
Class for log loss calculation (for classification).
Class for log loss calculation (for classification). This uses twice the binomial negative log likelihood, called "deviance" in Friedman (1999).
The log loss is defined as: 2 log(1 + exp(2 y F(x))) where y is a label in {1, 1} and F(x) is the model prediction for features x.
 Annotations
 @Since( "1.2.0" )

object
Losses
 Annotations
 @Since( "1.2.0" )

object
SquaredError extends Loss
Class for squared error loss calculation.
Class for squared error loss calculation.
The squared (L2) error is defined as: (y  F(x))**2 where y is the label and F(x) is the model prediction for features x.
 Annotations
 @Since( "1.2.0" )