Estimate a Resampled Confusion Matrix
## S3 method for class 'train': confusionMatrix(data, norm = "overall", dnn = c("Prediction", "Reference"), ...)
## S3 method for class 'rfe': confusionMatrix(data, norm = "overall", dnn = c("Prediction", "Reference"), ...)
## S3 method for class 'sbf': confusionMatrix(data, norm = "overall", dnn = c("Prediction", "Reference"), ...)
- an object of class
sbfthat did not use out-of-bag resampling or leave-one-out cross-validation.
- a character string indicating how the table entries should be normalized. Valid values are "none", "overall" or "average".
- a character vector of dimnames for the table
- not used here
train is used for tuning a model, it tracks the confusion matrix cell entries for the hold-out samples. These can be aggregated and used for diagnostic purposes. For
train, the matrix is estimated for the final model tuning parameters determined by
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 frequencies of samples on each of the cells (across all resamples).
norm = "overall" first divides the cell entries by the total number of data points in the table, then averages these percentages.
norm = "average" takes the raw, aggregate cell counts across resamples and divides by the number of resamples (i.e. to yield an average count for each cell).
- a list of class
table the normalized matrix nrom an echo fo the call text a character string with details about the resampling procedure (e.g. "Bootstrapped (25 reps) Confusion Matrix"
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")