ICPClassification: Class-conditional Inductive conformal classifier for...

In conformalClassification: Transductive and Inductive Conformal Predictions for Classification Problems

Description

Class-conditional Inductive conformal classifier for multi-class problems

Usage

ICPClassification(trainingSet, testSet, ratioTrain = 0.7, method = "rf",
  nrTrees = 100)

Arguments

trainingSet	Training set
testSet	Test set
ratioTrain	The ratio for proper training set
method	Method for modeling
nrTrees	Number of trees for RF

Value

The p-values

See Also

TCPClassification, parTCPClassification.

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)
#perfVlaues = pValues2PerfMetrics(pValues, testSet)
#print(perfVlaues)
#CPCalibrationPlot(pValues, testSet, "blue")

Example output

Loading required package: randomForest
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.
Loading required package: parallel
Loading required package: foreach
Loading required package: doParallel
Loading required package: iterators
Loading required package: mlbench

conformalClassification documentation built on May 2, 2019, 8:19 a.m.