confusionMatrix.train
Estimate a Resampled Confusion Matrix
Using a train
, rfe
, sbf
object, determine a confusion matrix based on the resampling procedure
 Keywords
 utilities
Usage
"confusionMatrix"(data, norm = "overall", dnn = c("Prediction", "Reference"), ...)
"confusionMatrix"(data, norm = "overall", dnn = c("Prediction", "Reference"), ...)
"confusionMatrix"(data, norm = "overall", dnn = c("Prediction", "Reference"), ...)
Arguments
 data
 An object of class
train
,rfe
,sbf
that did not use outofbag resampling or leaveoneout crossvalidation.  norm
 A character string indicating how the table entries should be normalized. Valid values are "none", "overall" or "average".
 dnn
 A character vector of dimnames for the table
 ...
 not used here
Details
When train
is used for tuning a model, it tracks the confusion matrix cell entries for the holdout samples. These can be aggregated and used for diagnostic purposes. For train
, the matrix is estimated for the final model tuning parameters determined by train
. For rfe
, the matrix is associated with the optimal number of variables.
There are several ways to show the table entries. Using norm = "none"
will show the aggregated counts of samples on each of the cells (across all resamples). For norm = "average"
, the average number of cell counts across resamples is computed (this can help evaluate how many holdout samples there were on average). The default is norm = "overall"
, which is equivalento to "average"
but in percentages.
Value

a list of class
 table
 the normalized matrix
 norm
 an echo fo the call
 text
 a character string with details about the resampling procedure (e.g. "Bootstrapped (25 reps) Confusion Matrix"
confusionMatrix.train
, confusionMatrix.rfe
or confusionMatrix.sbf
with elements
See Also
Examples
data(iris)
TrainData < iris[,1:4]
TrainClasses < iris[,5]
knnFit < train(TrainData, TrainClasses,
method = "knn",
preProcess = c("center", "scale"),
tuneLength = 10,
trControl = trainControl(method = "cv"))
confusionMatrix(knnFit)
confusionMatrix(knnFit, "average")
confusionMatrix(knnFit, "none")