\HeaderA{classifOutput-class}{Class "classifOutput": container for output of
classification procedures in R}{classifOutput.Rdash.class}
\aliasA{agnes-class}{classifOutput-class}{agnes.Rdash.class}
\aliasA{bagging-class}{classifOutput-class}{bagging.Rdash.class}
\aliasA{classbagg-class}{classifOutput-class}{classbagg.Rdash.class}
\aliasA{classifOutput}{classifOutput-class}{classifOutput}
\aliasA{cshell-class}{classifOutput-class}{cshell.Rdash.class}
\aliasA{diana-class}{classifOutput-class}{diana.Rdash.class}
\aliasA{dist-class}{classifOutput-class}{dist.Rdash.class}
\aliasA{gbm-class}{classifOutput-class}{gbm.Rdash.class}
\aliasA{groupIndex-class}{classifOutput-class}{groupIndex.Rdash.class}
\aliasA{ica-class}{classifOutput-class}{ica.Rdash.class}
\aliasA{ipredknn-class}{classifOutput-class}{ipredknn.Rdash.class}
\aliasA{kmeans-class}{classifOutput-class}{kmeans.Rdash.class}
\aliasA{knnP-class}{classifOutput-class}{knnP.Rdash.class}
\aliasA{lca-class}{classifOutput-class}{lca.Rdash.class}
\aliasA{lda-class}{classifOutput-class}{lda.Rdash.class}
\aliasA{logitboost-class}{classifOutput-class}{logitboost.Rdash.class}
\aliasA{membMat-class}{classifOutput-class}{membMat.Rdash.class}
\aliasA{MLLabel}{classifOutput-class}{MLLabel}
\aliasA{MLLabel-class}{classifOutput-class}{MLLabel.Rdash.class}
\aliasA{MLOutput}{classifOutput-class}{MLOutput}
\aliasA{MLOutput-class}{classifOutput-class}{MLOutput.Rdash.class}
\aliasA{MLScore}{classifOutput-class}{MLScore}
\aliasA{MLScore-class}{classifOutput-class}{MLScore.Rdash.class}
\aliasA{naiveBayes-class}{classifOutput-class}{naiveBayes.Rdash.class}
\aliasA{nnet.formula-class}{classifOutput-class}{nnet.formula.Rdash.class}
\aliasA{nsc-class}{classifOutput-class}{nsc.Rdash.class}
\aliasA{pam-class}{classifOutput-class}{pam.Rdash.class}
\aliasA{pamr-class}{classifOutput-class}{pamr.Rdash.class}
\aliasA{prcomp-class}{classifOutput-class}{prcomp.Rdash.class}
\aliasA{predClass-class}{classifOutput-class}{predClass.Rdash.class}
\aliasA{probArray-class}{classifOutput-class}{probArray.Rdash.class}
\aliasA{probMat-class}{classifOutput-class}{probMat.Rdash.class}
\aliasA{qda-class}{classifOutput-class}{qda.Rdash.class}
\aliasA{qualScore-class}{classifOutput-class}{qualScore.Rdash.class}
\aliasA{randomForest-class}{classifOutput-class}{randomForest.Rdash.class}
\aliasA{rpart-class}{classifOutput-class}{rpart.Rdash.class}
\aliasA{silhouetteB,classifOutput-method}{classifOutput-class}{silhouetteB,classifOutput.Rdash.method}
\aliasA{silhouetteVec-class}{classifOutput-class}{silhouetteVec.Rdash.class}
\aliasA{slda-class}{classifOutput-class}{slda.Rdash.class}
\aliasA{SOMBout-class}{classifOutput-class}{SOMBout.Rdash.class}
\aliasA{svm-class}{classifOutput-class}{svm.Rdash.class}
\keyword{classes}{classifOutput-class}
\begin{Description}\relax
container with uniform contents for classifier outputs
\end{Description}
\begin{Section}{Objects from the Class}
Objects can be created by calls of the form \code{new("classifOutput", ...)}.
These objects include label vectors for test set elements, scores on labeling
events, call history information, native classifier object access,
and a distance matrix for test set feature vectors.
\end{Section}
\begin{Section}{Slots}
\describe{
\item[\code{method}:] Object of class \code{"character"} tag identifying
classification method in use 
\item[\code{predLabels}:] Object of class \code{"MLLabel"} vector
of labels predicted for test cases 
\item[\code{predScores}:] Object of class \code{"MLScore"} structure
of scores on classification events (for example, vote proportions
or fitted probabilities of class membership

\item[\code{call}:] Object of class \code{"call"} call engendering the
classifOutput object 
\item[\code{classifRobject}:] Object of class \code{"ANY"} fitted model
object 
\item[\code{distMat}:] Object of class \code{"dist"} distance matrix 
of test set feature vectors 
}
\end{Section}
\begin{Section}{Methods}
\describe{
\item[show] \code{signature(object = "classifOutput")}: ... 
\item[silhouetteB] \code{signature(out = "classifOutput")}: ... 
}
\end{Section}
\begin{Author}\relax
VJ Carey <stvjc@channing.harvard.edu>
\end{Author}
\begin{Examples}
\begin{ExampleCode}
# access and trim an ExpressionSet
library(golubEsets)
data(Golub_Merge)
smallG <- Golub_Merge[1:60,]
# set a PRNG seed for reproducibilitiy
set.seed(1234) # needed for nnet initialization
# now run the classifiers
f1 = knnB( smallG, "ALL.AML", 1:40 )
f1
confuMat(f1)
\end{ExampleCode}
\end{Examples}

