\HeaderA{confuMat-methods}{Methods for Function confuMat in Package `MLInterfaces'}{confuMat.Rdash.methods}
\aliasA{confuMat}{confuMat-methods}{confuMat}
\aliasA{confuMat,classifOutput-method}{confuMat-methods}{confuMat,classifOutput.Rdash.method}
\keyword{methods}{confuMat-methods}
\keyword{classif}{confuMat-methods}
\begin{Description}\relax
compute the confusion matrix for a classifier's output
\end{Description}
\begin{Section}{Methods}
\describe{
\item[obj = "classifOutput"] Typically, an instance of class \code{"\LinkA{classifOutput}{classifOutput.Rdash.class}"}
is built on a ``training'' subset of the input data, and the ``test'' subset's
true class labels are available.  This method cross-tabulates the true labels
of the test set against the predictions of the fitted classifier.
}
\end{Section}
\begin{Examples}
\begin{ExampleCode}
library(golubEsets)
data(Golub_Merge)
smallG <- Golub_Merge[1:50,]
k1 <- knnB(smallG, "ALL.AML", 1:30)
confuMat(k1)
\end{ExampleCode}
\end{Examples}

