# 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

```
## 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"), ...)

##### Arguments

- data
- an object of class
`train`

,`rfe`

,`sbf`

that did not use out-of-bag resampling or leave-one-out cross-validation. - 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 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 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).

##### Value

- 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"

##### 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")
```

*Documentation reproduced from package caret, version 6.0-52, License: GPL (>= 2)*