loss functions: Classification and Regression Loss functions

Artikel
02/28/2023

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

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

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