confusionMatrix.train {caret} | R Documentation |
Using a train
, rfe
, sbf
object, determine a confusion matrix based on the resampling procedure
## 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"), ...)
data |
An object of class |
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 |
When 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 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.
a list of class confusionMatrix.train
, confusionMatrix.rfe
or confusionMatrix.sbf
with elements
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" |
Max Kuhn
confusionMatrix
, train
, rfe
, sbf
, trainControl
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")