caret (version 2.27)

plotClassProbs: Plot Predicted Probabilities in Classification Models

Description

This function takes an object (preferably from the function extractProb) and creates a lattice plot. If the call to extractProb included test data, these data are shown, but if unknowns were also included, these are not plotted

Usage

plotClassProbs(object, ...)

Arguments

object

an object (preferably from the function extractProb. There should be columns for each level of the class factor and columns named obs, pred, model (e.g. "rpart"

...

parameters to pass to histogram

Value

• A lattice object. Note that the plot has to be printed to be displayed (especially in a loop).

Examples

data(iris)
set.seed(90)
inTrain <- sample(1:dim(iris)[1], 100)

trainData <- iris[inTrain,]
testData <- iris[-inTrain,]

rpartFit <- train(trainData[, -5], trainData[, 5], "rpart", tuneLength = 15)
plsFit <- train(trainData[, -5], trainData[, 5], "pls", tuneLength = 9)
predProbs <- extractProb(list(plsFit, rpartFit),
   testX = testData[, -5], testY = testData[, 5])


plotClassProbs(predProbs)
plotClassProbs(predProbs[predProbs$model == "pls",])
plotClassProbs(predProbs[predProbs$model == "pls" & predProbs$dataType == "Test",])

# example plot as if this were a two class problem:
trainData <- iris[-(1:50),]
plsFit <- train(trainData[, -5], factor(trainData[, 5]), "pls", tuneLength = 9)
predProbs <- extractProb(list(plsFit))
plotClassProbs(predProbs)