\HeaderA{classifierOutput-class}{Class "classifierOutput"}{classifierOutput.Rdash.class}
\aliasA{fsHistory,classifierOutput-method}{classifierOutput-class}{fsHistory,classifierOutput.Rdash.method}
\aliasA{RObject}{classifierOutput-class}{RObject}
\aliasA{RObject,classifierOutput-method}{classifierOutput-class}{RObject,classifierOutput.Rdash.method}
\aliasA{show,classifierOutput-method}{classifierOutput-class}{show,classifierOutput.Rdash.method}
\aliasA{testPredictions}{classifierOutput-class}{testPredictions}
\methaliasA{testPredictions}{classifierOutput-class}{testPredictions}
\aliasA{testPredictions,classifierOutput-method}{classifierOutput-class}{testPredictions,classifierOutput.Rdash.method}
\aliasA{testScores}{classifierOutput-class}{testScores}
\aliasA{testScores,classifierOutput-method}{classifierOutput-class}{testScores,classifierOutput.Rdash.method}
\aliasA{trainPredictions}{classifierOutput-class}{trainPredictions}
\aliasA{trainPredictions,classifierOutput-method}{classifierOutput-class}{trainPredictions,classifierOutput.Rdash.method}
\keyword{classes}{classifierOutput-class}
\begin{Description}\relax
This class summarizes the output values from different classifiers.
\end{Description}
\begin{Section}{Objects from the Class}
Objects are typically created during the application of a supervised 
machine learning algorithm to data and are the value returned.  It is very
unlikely that any user would create such an object by hand.
\end{Section}
\begin{Section}{Slots}
\describe{
\item[\code{testOutcomes}:] Object of class \code{"factor"} that
lists the actual outcomes in the records on the test set 
\item[\code{testPredictions}:] Object of class \code{"factor"} that
lists the predictions of outcomes in the test set 
\item[\code{testScores}:] Object of class \code{"ANY"} -- this
element will include matrices or vectors or arrays that include
information that is typically related to the posterior probability
of occupancy of the predicted class or of all classes.  The actual
contents of this slot can be determined by inspecting the converter
element of the learnerSchema used to select the model. 
\item[\code{trainOutcomes}:] Object of class \code{"factor"} that
lists the actual outcomes in records on the training set 
\item[\code{trainPredictions}:] Object of class \code{"factor"} that
lists the predicted outcomes in the training set 
\item[\code{trainScores}:] Object of class \code{"ANY"} see
the description of \code{testScores} above; the same information
is returned, but applicable to the training set records.

\item[\code{RObject}:] Object of class \code{"ANY"} -- when
the \code{trainInd} parameter of the \code{MLearn} call is
numeric, this slot holds
the return value of the underlying R function that carried out
the predictive modeling.  For example, if \code{rpartI} was used
as MLearn \code{method}, \code{Robject} holds an instance of the
\code{rpart} S3 class, and \code{plot} and \code{text} methods
can be applied to this.  When the \code{trainInd} parameter
of the \code{MLearn} call is an instance of
\code{\LinkA{xvalSpec}{xvalSpec.Rdash.class}}, this slot holds a \code{list} of
results of cross-validatory iterations.  Each element of this
list has two elements: \code{test.idx}, giving the numeric
indices of the test cases for the associated cross-validation
iteration, and \code{mlans}, which is the \code{\LinkA{classifierOutput}{classifierOutput.Rdash.class}}
for the associated iteration.  See the example for an illustration
of 'digging out' the predicted probabilities associated with each
cross-validation iteration executed through an xvalSpec specification. 
\item[\code{call}:] Object of class \code{"call"} -- records the
call used to generate the classifierOutput RObject 
\item[\code{embeddedCV}:] logical indicating whether the learning
engine has cross-validation embedded within
\item[\code{fsHistory}:] list of feature names included in the
feature selection process iterations
\item[\code{learnerSchema}:] audit of what was used to 
schematize the learning process for this run
}
\end{Section}
\begin{Section}{Methods}
\describe{
\item[confuMat] \code{signature(obj = "classifierOutput")}: Compute
the confusion matrix for test records. 
\item[confuMatTrain] \code{signature(obj = "classifierOutput")}: Compute
the confusion matrix for training set.  Typically yields optimistically biased
information on misclassification rate. 
\item[RObject] \code{signature(obj = "classifierOutput")}: The R object
returned by the underlying classifier. This can then be passed on to
specific methods for those objects, when they exist. 
\item[show] \code{signature(object = "classifierOutput")}: A print method
that provides a summary of the output of the classifier. 
\item[testScores] \code{signature(object = "classifierOutput")}: ... 
\item[testPredictions] \code{signature(object = "classifierOutput")}: Print
the predicted classes for each sample/individual in the test set. 
\item[trainPredictions] \code{signature(object = "classifierOutput")}: Print
the predicted classes for each sample/individual in the training set. 
\item[fsHistory] \code{signature(object = "classifierOutput")}: ... 
}
\end{Section}
\begin{Author}\relax
V. Carey
\end{Author}
\begin{Examples}
\begin{ExampleCode}
showClass("classifierOutput")
library(golubEsets)
data(Golub_Train) # now cross-validate a neural net
set.seed(1234)
xv5 = xvalSpec("LOG", 5, balKfold.xvspec(5))
m2 = MLearn(ALL.AML~., Golub_Train[1000:1050,], nnetI, xv5, 
   size=5, decay=.01, maxit=1900 )
testScores(RObject(m2)[[1]]$mlans)
alls = lapply(RObject(m2), function(x) testScores(x$mlans))
\end{ExampleCode}
\end{Examples}

