Correct Way of Using update.train?

[terrytangyuan]

@topepo Is this the correct way to continue training from an existing model? Seems like update only updates the model object to some most recent compatible version without actually calling fit using the new dataset? I think I am missing something important here.

previousModel <- readRDS("....xxx.rds")  # load previously saved model object
previousModel$trainingData <- trainData # change training data to new data
newModel <- update(object = previousModel)

[zachmayer]

I updated your comment to include the issue template you deleted. Please update each code chunk with the appropriate runnable code to replicate your issue, and then go through the checklist and check off the boxes as you complete the tasks.

[zachmayer]

ok I see the issue: update.train is meant for updating the tuning parameters, not the training data. So if the tuning parameters don't change, the model won't be re-fit.

[terrytangyuan]

@zachmayer I see. Thanks for your quick response! Could I add a forceRefit option in update.train? I think it makes more sense.

[zachmayer]

That sounds like a good idea to me— make a PR!

[Artem-fb]

@terrytangyuan , hi! so there is no way to quickly refit model with new data? I want to re-fit it with each new row, like online learning

[terrytangyuan]

Probably not directly with caret. You can try continue training through XGBoost.