Description Usage Arguments Details Value Author(s) See Also Examples

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.

1 |

`learn` |
Object of class |

`predict` |
Object of class |

`...` |
Additional parameters required for the specific kind of classificaiton model that will be constructed. See 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`

.

Returns an object of the `Modeler-class`

.

Kevin R. Coombes <krc@silicovore.com>

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.

1 2 3 4 | ```
learnNNET
predictNNET
modelerNNET <- Modeler(learnNNET, predictNNET, size=5)
modelerNNET
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.