Modeler: Constructor for "Modeler" objects

In Modeler: Classes and Methods for Training and Using Binary Prediction Models

Constructor for "Modeler" objects

Description

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.

Usage

Modeler(learn, predict, ...)

Arguments

learn	Object of class "function" that will be used to fit the model to a data set. See learn for details.
predict	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.

Details

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.

Value

Returns an object of the Modeler-class.

Author(s)

Kevin R. Coombes <[e-mail]>

See Also

See the descriptions of the learn function and the predict,FittedModel-method 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.

Examples

learnNNET
predictNNET
modelerNNET <- Modeler(learnNNET, predictNNET, size=5)
modelerNNET

Modeler documentation built on April 11, 2025, 3:10 p.m.