plotClassProbs
From caret v3.45
by Max Kuhn
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, ...)
Arguments
- object
- an object (preferably from the function
extractProb
. There should be columns for each level of the class factor and columns namedobs
,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)
ldaFit <- train(trainData[, -5], trainData[, 5], "lda")
predProbs <- extractProb(list(ldaFit, rpartFit),
testX = testData[, -5], testY = testData[, 5])
plotClassProbs(predProbs)
plotClassProbs(predProbs[predProbs$model == "lda",])
plotClassProbs(predProbs[predProbs$model == "lda" & predProbs$dataType == "Test",])
Community examples
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