:: DeveloperApi :: Trait for adding "pluggable" loss functions for the gradient boosting algorithm.
:: 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.
:: 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.
:: 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.
:: DeveloperApi :: Trait for adding "pluggable" loss functions for the gradient boosting algorithm.