Estimate a Resampled Confusion Matrix
## S3 method for class 'train'
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
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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.