Plot Predicted Probabilities in Classification Models
This function takes an object (preferably from the function
extractProb
) and creates a lattice plot.
plotClassProbs(object, plotType = "histogram", useObjects = FALSE, ...)
object |
an object (preferably from the function
|
plotType |
either "histogram" or "densityplot" |
useObjects |
a logical; should the object name (if any) be used as a conditioning variable? |
... |
parameters to pass to |
If the call to extractProb
included test data, these data are
shown, but if unknowns were also included, these are not plotted
A lattice object. Note that the plot has to be printed to be displayed (especially in a loop).
Max Kuhn
## Not run: 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, fL = 0)) nbFit2 <- train(trainData, trainY, "nb", trControl = ctrl, tuneGrid = data.frame(usekernel = FALSE, fL = 0)) 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)) ## End(Not run)
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