loss-methods: Loss of a classifier or regression function

In jkrijthe/RSSL: Implementations of Semi-Supervised Learning Approaches for Classification

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 signature 'LeastSquaresClassifier'
loss(object, newdata, y = NULL, ...)

## S4 method for signature 'NormalBasedClassifier'
loss(object, newdata, y = NULL)

## S4 method for signature 'LogisticRegression'
loss(object, newdata, y = NULL)

## S4 method for signature 'KernelLeastSquaresClassifier'
loss(object, newdata, y = NULL, ...)

## S4 method for signature 'LinearSVM'
loss(object, newdata, y = NULL)

## S4 method for signature 'LogisticLossClassifier'
loss(object, newdata, y = NULL, ...)

## S4 method for signature 'MajorityClassClassifier'
loss(object, newdata, y = NULL)

## S4 method for signature 'SVM'
loss(object, newdata, y = NULL)

## S4 method for signature 'SelfLearning'
loss(object, newdata, y = NULL, ...)

## S4 method for signature 'USMLeastSquaresClassifier'
loss(object, newdata, y = NULL, ...)

## S4 method for signature 'svmlinClassifier'
loss(object, newdata, y = NULL)

Arguments

object	Classifier; Trained Classifier
...	additional parameters
newdata	data.frame; object with test data
y	factor; True classes of the test data

Value

numeric; the total loss on the test data

jkrijthe/RSSL documentation built on Jan. 13, 2024, 1:56 a.m.