Model for classification and regression

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Description

The Modeler package provides generic tools for learning models and making predictions in the context of high-throughput data sets such as those produced by gene expression microarrays. In order to use this package , 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 Modeler function along with the full training data set.

Details

Package: Modeler
Type: Package
Version: 2.0.0
Date: 2013-07-05
License: Artistic-2.0
LazyLoad: yes

Author(s)

Kevin R. Coombes krc@silicovore.com

See Also

The following classification methods have been adapted to use the Modeler class: K nearest neighbors (learnKNN), recursive partitioning and regression trees (learnRPART),