confuMat-methods {MLInterfaces} | R Documentation

## Compute the confusion matrix for a classifier.

### Description

This function will compute the confusion matrix for a classifier's output

### Methods

obj = "classifOutput", ...

  Typically, an instance of class `"classifierOutput"` is built on a training subset of the input data. The model is then used to predict the class of samples in the test set. When the true class labels for the test set are available the confusion matrix is the cross-tabulation of the true labels of the test set against the predictions from the classifier. An optional `t` score threshold can also be specified.

obj = "classifierOutput", type="character", ...

  For instances of classifierOutput, it is possible to specify the `type` of confusion matrix desired. The default is `test`, which tabulates classes from the test set against the associated predictions. If `type` is `train`, the training class vector is tabulated against the predictions on the training set. An optional `t` score threshold can also be specified.

obj = "classifierOutput", type="numeric"

  For instances of classifierOutput, it is possible to specify the minimum score feature classification threshold. Features with a score less than the threshold are classified as `NA` in the confustion `train` or `test` confusion matrix.

### Examples

    library(golubEsets)
    data(Golub_Merge)
    smallG <- Golub_Merge[101:150,]
    k1 <- MLearn(ALL.AML~., smallG, knnI(k=1), 1:30)
    confuMat(k1)
    confuMat(k1, "train")

[Package MLInterfaces version 1.70.0 Index]