plotClassProbs

0th

Percentile

Plot Predicted Probabilities in Classification Models

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

Keywords
hplot
Usage
plotClassProbs(object,
               plotType = "histogram",
               useObjects = FALSE,
               ...)
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"
plotType
either "histogram" or "densityplot"
useObjects
a logical; should the object name (if any) be used as a conditioning variable?
...
parameters to pass to histogram or densityplot
Value

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

Aliases
  • plotClassProbs
Examples
data(mdrr)
set.seed(90)
inTrain <- createDataPartition(mdrrClass, p = .5)[[1]]

trainData <- mdrrDescr[inTrain,1:20]
testData <- mdrrDescr[-inTrain,1:20]

trainY <- mdrrClass[inTrain]
testY <- mdrrClass[-inTrain]

ctrl <- trainControl(method = "cv")

nbFit1 <- train(trainData, trainY, "nb",
                trControl = ctrl,
                tuneGrid = data.frame(.usekernel = TRUE))

nbFit2 <- train(trainData, trainY, "nb",
                trControl = ctrl,
                tuneGrid = data.frame(.usekernel = FALSE))


models <- list(para = nbFit2,
               nonpara = nbFit1)

predProbs <- extractProb(models,
                         testX = testData, 
                         testY = testY)

plotClassProbs(predProbs,
               useObjects = TRUE)
plotClassProbs(predProbs,
               subset = object == "para" & dataType == "Test")
plotClassProbs(predProbs,
               useObjects = TRUE,
               plotType = "densityplot",
               auto.key = list(columns = 2))
Documentation reproduced from package caret, version 4.41, License: GPL-2

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