update.train: Update or Re-fit a Model

In caret: Classification and Regression Training

Update or Re-fit a Model

Description

update allows a user to over-ride the tuning parameter selection process by specifying a set of tuning parameters or to update the model object to the latest version of this package.

Usage

## S3 method for class 'train'
update(object, param = NULL, ...)

Arguments

object	an object of class train
param	a data frame or named list of all tuning parameters
...	not currently used

Details

If the model object was created with version 5.17-7 or earlier, the underlying package structure was different. To make old train objects consistent with the new structure, use param = NULL to get the same object back with updates.

To update the model parameters, the training data must be stored in the model object (see the option returnData in trainControl. Also, all tuning parameters must be specified in the param slot. All other options are held constant, including the original pre-processing (if any), options passed in using code... and so on. When printing, the verbiage "The tuning parameter was set manually." is used to describe how the tuning parameters were created.

Value

a new train object

Author(s)

Max Kuhn

See Also

train, trainControl

Examples

## Not run: 
data(iris)
TrainData <- iris[,1:4]
TrainClasses <- iris[,5]

knnFit1 <- train(TrainData, TrainClasses,
                 method = "knn",
                 preProcess = c("center", "scale"),
                 tuneLength = 10,
                 trControl = trainControl(method = "cv"))

update(knnFit1, list(.k = 3))

## End(Not run)

caret documentation built on March 31, 2023, 9:49 p.m.