binaryClassificationLoss: Loss functions for binary classification

Description Usage Arguments Value Functions References See Also Examples

Description

Loss functions for binary classification

Usage

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logisticLoss(x, y, loss.weights = 1)

rocLoss(x, y)

fbetaLoss(x, y, beta = 1)

hingeLoss(x, y, loss.weights = 1)

Arguments

x

matrix of training instances (one instance by row)

y

a logical vector representing the training labels for each instance in x

loss.weights

numeric vector of loss weights to incure for each instance of x. Vector length should match length(y), but values are cycled if not of identical size.

beta

a numeric value setting the beta parameter is the f-beta score

Value

a function taking one argument w and computing the loss value and the gradient at point w

Functions

References

Teo et al. A Scalable Modular Convex Solver for Regularized Risk Minimization. KDD 2007

See Also

nrbm

Examples

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  x <- cbind(intercept=100,data.matrix(iris[1:2]))
  w <- nrbm(hingeLoss(x,iris$Species=="setosa"));predict(w,x)
  w <- nrbm(logisticLoss(x,iris$Species=="setosa"));predict(w,x)
  w <- nrbm(rocLoss(x,iris$Species=="setosa"));predict(w,x)
  w <- nrbm(fbetaLoss(x,iris$Species=="setosa"));predict(w,x)

bmrm documentation built on May 2, 2019, 2:49 p.m.