CrossValidate package provides generic tools for performing
cross-validation on classification methods in the context of high-throughput
data sets such as those produced by gene expression microarrays. In order to
use a classifier with this implementation of cross-validation, you must first
prepare a pair of functions (one for learning models from training data, and
one for making predictions on test data). These functions, along with any
required meta-parameters, are used to create an object of the
Modeler-class. That object is then passed to the
CrossValidate function along with the full training data set. The
full data set is then repeatedly split into its own training and test sets;
you can specify the fraction to be used for training and the number of
iterations. The result is a detailed look at the accuracy, sensitivity,
specificity, and positive and negative predictive value of the model, as
estimated by cross-validation.
Kevin R. Coombes firstname.lastname@example.org
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