CPErrorRate {conformalClassification}

Computes error rate of a conformal predictor, which is defined as the ratio of predictions with missing true class lables over the size of the testset

Description

Computes error rate of a conformal predictor, which is defined as the ratio of predictions with missing true class lables over the size of the testset

Usage

CPErrorRate(matPValues, testLabels, sigfLevel = 0.05)

Arguments

matPValues

Matrix of p-values

testLabels

True labels for the test-set

sigfLevel

Significance level

Value

The error rate

See Also

CPCalibrationPlot, CPEfficiency, CPValidity, CPObsFuzziness.

Examples

## load the library
library(mlbench)
#library(caret)
library(conformalClassification)

## load the DNA dataset
data(DNA)
originalData <- DNA

## make sure first column is always the label and class labels are always 1, 2, ...
nrAttr = ncol(originalData) #no of attributes
tempColumn = originalData[, 1]
originalData[, 1] = originalData[, nrAttr]
originalData[, nrAttr] = tempColumn
originalData[, 1] = as.factor(originalData[, 1])
originalData[, 1] = as.numeric(originalData[, 1])

## partition the data into training and test set
#result = createDataPartition(originalData[, 1], p = 0.8, list = FALSE)
size = nrow(originalData)
result = sample(1:size,  0.8*size)

trainingSet = originalData[result, ]
testSet = originalData[-result, ]

##ICP classification
pValues = ICPClassification(trainingSet, testSet)
testLabels = testSet[,1]
CPErrorRate(pValues, testLabels)