TCPClassification {conformalClassification} | R Documentation |
Class-conditional transductive conformal classifier for multi-class problems
Description
Class-conditional transductive conformal classifier for multi-class problems
Usage
TCPClassification(trainSet, testSet, method = "rf", nrTrees = 100)
Arguments
testSet |
Test set |
method |
Method for modeling |
nrTrees |
Number of trees for RF |
trainSet |
Training set |
Value
The p-values
See Also
parTCPClassification
.
ICPClassification
.
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)
#trainingSet = originalData[result, ]
#testSet = originalData[-result, ]
##reduce the size of the training set, because TCP is slow
#result = createDataPartition(trainingSet[, 1], p=0.8, list=FALSE)
#trainingSet = trainingSet[-result, ]
##TCP classification
#pValues = TCPClassification(trainingSet, testSet)
#perfVlaues = pValues2PerfMetrics(pValues, testSet)
#print(perfVlaues)
#CPCalibrationPlot(pValues, testSet, "blue")
#not run
[Package conformalClassification version 1.0.0 Index]