The Modeler-class represents (parametrized but not yet
fit) statistical models that can predict binary outcomes. The
Modeler function is used to construct objects of this class.
Modeler(learn, predict, ...)Object of class "function" that will be used to fit the model
to a data set. See learn for details.
Object of class "function" that will be used to make
predictions on new data from a fitted model. See
predict for details.
Additional parameters required for the specific kind of classificaiton model that will be constructed. See Details.
Returns an object of the Modeler-class.
Objects of the Modeler-class provide a general
abstraction for classification models that can be learned from one
data set and then applied to a new data set. Each type of classifier
is likely to have its own specific parameters. For instance, a
K-nearest neighbors classifier requires you to specify k. The
more complex classifier, PCA-LR has many more parameters, including
the false discovery rate (alpha) used to select features and
the percentage of variance (perVar) that should be explained by
the number of principal components created from those features. All
additional parameters should be suplied as named arguments to the
Modeler constructor; these additional parameters will be
bundled into a list and inserted into the params slot of the
resulting object of the Modeler-class.
See the descriptions of the learn function and
the predict method for details on how to fit models on
training data and make predictions on new test data.
See the description of the FittedModel-class for details
about the kinds of objects produced by learn.
See Modeler-package for a list of the kinds of
classifiers that have been adapted for use in this generic framework.
# NOT RUN {
learnNNET
predictNNET
modelerNNET <- Modeler(learnNNET, predictNNET, size=5)
modelerNNET
# }
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