loss functions: Classification and Regression Loss functions
The loss functions for classification and regression.
Usage
expLoss(beta = 1, ...)
hingeLoss(margin = 1, ...)
logLoss(...)
smoothHingeLoss(smoothingConst = 1, ...)
poissonLoss(...)
squaredLoss(...)
Arguments
beta
Specifies the numeric value of beta (dilation). The default value is 1.
margin
Specifies the numeric margin value. The default value is 1.
smoothingConst
Specifies the numeric value of the smoothing constant. The default value is 1.
...
hidden argument.
Details
A loss function measures the discrepancy between the prediction of a machine learning algorithm and the supervised output and represents the cost of being wrong.
The classification loss functions supported are:
logLoss
expLoss
hingeLoss
smoothHingeLoss
The regression loss functions supported are:
poissonLoss
squaredLoss
.
Value
A character string defining the loss function.
Author(s)
Microsoft Corporation Microsoft Technical Support
See also
Examples
train <- function(lossFunction) {
result <- rxFastLinear(isCase ~ age + parity + education + spontaneous + induced,
transforms = list(isCase = case == 1), lossFunction = lossFunction,
data = infert,
type = "binary")
coef(result)[["age"]]
}
age <- list()
age$LogLoss <- train(logLoss())
age$LogLossHinge <- train(hingeLoss())
age$LogLossSmoothHinge <- train(smoothHingeLoss())
age