Description Usage Arguments Value See Also Examples

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

1 | ```
CPValidity(matPValues = NULL, testLabels = NULL)
``` |

`matPValues` |
Matrix of p-values |

`testLabels` |
True labels for the test-set |

The deviation from exact validity

`CPCalibrationPlot`

,
`CPEfficiency`

,
`CPErrorRate`

,
`CPObsFuzziness`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
## 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)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.