RSSL (version 0.9.7)

loss: Loss of a classifier or regression function

Description

Hinge loss on new objects of a trained LinearSVM

Hinge loss on new objects of a trained SVM

Usage

loss(object, ...)
# S4 method for LeastSquaresClassifier
loss(object, newdata, y = NULL, ...)
# S4 method for NormalBasedClassifier
loss(object, newdata, y = NULL)
# S4 method for LogisticRegression
loss(object, newdata, y = NULL)
# S4 method for KernelLeastSquaresClassifier
loss(object, newdata, y = NULL, ...)
# S4 method for LinearSVM
loss(object, newdata, y = NULL)
# S4 method for LogisticLossClassifier
loss(object, newdata, y = NULL, ...)
# S4 method for MajorityClassClassifier
loss(object, newdata, y = NULL)
# S4 method for SVM
loss(object, newdata, y = NULL)
# S4 method for SelfLearning
loss(object, newdata, y = NULL, ...)
# S4 method for USMLeastSquaresClassifier
loss(object, newdata, y = NULL, ...)
# S4 method for svmlinClassifier
loss(object, newdata, y = NULL)

Value

numeric; the total loss on the test data

Arguments

object

Classifier; Trained Classifier

...

additional parameters

newdata

data.frame; object with test data

y

factor; True classes of the test data