confusionMatrix.train | 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"), ... )

`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")

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