Description Usage Arguments Value Functions References See Also Examples
Loss functions for binary classification
1 2 3 4 5 6 7  logisticLoss(x, y, loss.weights = 1)
rocLoss(x, y)
fbetaLoss(x, y, beta = 1)
hingeLoss(x, y, loss.weights = 1)

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 fbeta score 
a function taking one argument w and computing the loss value and the gradient at point w
logisticLoss
: logistic regression
rocLoss
: Find linear weights maximize area under its ROC curve
fbetaLoss
: Fbeta score loss function
hingeLoss
: Hinge Loss for Linear Support Vector Machine (SVM)
Teo et al. A Scalable Modular Convex Solver for Regularized Risk Minimization. KDD 2007
nrbm
1 2 3 4 5 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.