loss

Value Members

1. object AbsoluteError extends Loss

   

   :: DeveloperApi :: Class for absolute error loss calculation (for regression).

   :: DeveloperApi :: 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" ) @DeveloperApi()

2. object LogLoss extends ClassificationLoss

   

   :: DeveloperApi :: Class for log loss calculation (for classification).

   :: DeveloperApi :: 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" ) @DeveloperApi()

3. object Losses

   

   Annotations

   @Since( "1.2.0" )

4. object SquaredError extends Loss

   

   :: DeveloperApi :: Class for squared error loss calculation.

   :: DeveloperApi :: 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" ) @DeveloperApi()