plotClassProbs
From caret v4.34
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,
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 namedobs
,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
ordensityplot
Value
- A lattice object. Note that the plot has to be printed to be displayed (especially in a loop).
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))
Community examples
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