CPValidity function

Computes the deviation from exact validity as the Euclidean norm of the difference of the observed error and the expected error

CPValidity(matPValues = NULL, testLabels = NULL)

Arguments

• matPValues: Matrix of p-values
• testLabels: True labels for the test-set

Returns

The deviation from exact validity

See Also

CPCalibrationPlot, CPEfficiency, CPErrorRate, 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]
CPValidity(pValues, testLabels)